Compositions, Methods and Kits for Diagnosis of Lung Cancer

ABSTRACT

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC)

RELATED APPLICATIONS

This application is a continuation-in-part of the U.S. application Ser.No. 13/724,823 filed Dec. 21, 2012, which claims priority and benefit ofU.S. Provisional Application No. 61/578,712 filed Dec. 21, 2011, U.S.Provisional Application No. 61/589,920 filed Jan. 24, 2012, U.S.Provisional Application No. 61/676,859 filed Jul. 27, 2012 and U.S.Provisional Application No. 61/725,153 filed Nov. 12, 2012, the contentsof each of which are incorporated herein by reference in theirentireties.

BACKGROUND

Lung conditions and particularly lung cancer present significantdiagnostic challenges. In many asymptomatic patients, radiologicalscreens such as computed tomography (CT) scanning are a first step inthe diagnostic paradigm. Pulmonary nodules (PNs) or indeterminatenodules are located in the lung and are often discovered duringscreening of both high risk patients or incidentally. The number of PNsidentified is expected to rise due to increased numbers of patients withaccess to health care, the rapid adoption of screening techniques and anaging population. It is estimated that over 3 million PNs are identifiedannually in the US. Although the majority of PNs are benign, some aremalignant leading to additional interventions. For patients consideredlow risk for malignant nodules, current medical practice dictates scansevery three to six months for at least two years to monitor for lungcancer. The time period between identification of a PN and diagnosis isa time of medical surveillance or “watchful waiting” and may inducestress on the patient and lead to significant risk and expense due torepeated imaging studies. If a biopsy is performed on a patient who isfound to have a benign nodule, the costs and potential for harm to thepatient increase unnecessarily. Major surgery is indicated in order toexcise a specimen for tissue biopsy and diagnosis. All of theseprocedures are associated with risk to the patient including: illness,injury and death as well as high economic costs.

Frequently, PNs cannot be biopsied to determine if they are benign ormalignant due to their size and/or location in the lung. However, PNsare connected to the circulatory system, and so if malignant, proteinmarkers of cancer can enter the blood and provide a signal fordetermining if a PN is malignant or not.

Diagnostic methods that can replace or complement current diagnosticmethods for patients presenting with PNs are needed to improvediagnostics, reduce costs and minimize invasive procedures andcomplications to patients. The present invention provides novelcompositions, methods and kits for identifying protein markers toidentify, diagnose, classify and monitor lung conditions, andparticularly lung cancer. The present invention uses a blood-basedmultiplexed assay to distinguish benign pulmonary nodules from malignantpulmonary nodules to classify patients with or without lung cancer. Thepresent invention may be used in patients who present with symptoms oflung cancer, but do not have pulmonary nodules.

SUMMARY

The present invention provides a method of determining the likelihoodthat a lung condition in a subject is cancer by measuring an abundanceof a panel of proteins in a sample obtained from the subject;calculating a probability of cancer score based on the proteinmeasurements and ruling out cancer for the subject if the score is lowerthan a pre-determined score. When cancer is ruled out, the subject doesnot receive a treatment protocol. Treatment protocols include forexample pulmonary function test (PFT), pulmonary imaging, a biopsy, asurgery, a chemotherapy, a radiotherapy, or any combination thereof. Insome embodiments, the imaging is an x-ray, a chest computed tomography(CT) scan, or a positron emission tomography (PET) scan.

The present invention further provides a method of ruling in thelikelihood of cancer for a subject by measuring an abundance of panel ofproteins in a sample obtained from the subject, calculating aprobability of cancer score based on the protein measurements and rulingin the likelihood of cancer for the subject if the score is higher thana pre-determined score.

In another aspect, the invention further provides a method ofdetermining the likelihood of the presence of a lung condition in asubject by measuring an abundance of panel of proteins in a sampleobtained from the subject, calculating a probability of cancer scorebased on the protein measurements and concluding the presence of saidlung condition if the score is equal or greater than a pre-determinedscore. The lung condition is lung cancer such as for example, non-smallcell lung cancer (NSCLC). The subject is at risk of developing lungcancer.

In some embodiments, the panel includes at least 3 proteins selectedfrom ALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN,PRDX1 and CD14. Optionally, the panel further includes at least oneprotein selected from BGH3, COIA1, TETN, GRP78, PRDX, FIBA and GSLG1.

In some embodiments, the panel includes at least 4 proteins selectedfrom ALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN,PRDX1 and CD14.

In a preferred embodiment, the panel comprises LRP1, COIA1, ALDOA, andLG3BP.

In another preferred embodiment, the panel comprises LRP1, COIA1, ALDOA,LG3BP, BGH3, PRDX1, TETN, and ISLR.

In yet another preferred embodiment, the panel comprises LRP1, COIA1,ALDOA, LG3BP, BGH3, PRDX1, TETN, ISLR, TSP1, GRP78, FRIL, FIBA andGSLG1.

The subject has or is suspected of having a pulmonary nodule. Thepulmonary nodule has a diameter of less than or equal to 3 cm. In oneembodiment, the pulmonary nodule has a diameter of about 0.8 cm to 2.0cm.

The score is calculated from a logistic regression model applied to theprotein measurements. For example, the score is determined asP_(s)=1/[1+exp(−α−Σ_(i=1) ^(N)β_(i)*{hacek over (I)}_(i,s))], where{hacek over (I)}_(i,s) is logarithmically transformed and normalizedintensity of transition i in said sample (s), β_(i) is the correspondinglogistic regression coefficient, α was a panel-specific constant, and Nwas the total number of transitions in said panel.

In various embodiments, the method of the present invention furthercomprises normalizing the protein measurements. For example, the proteinmeasurements are normalized by one or more proteins selected from PEDF,MASP1, GELS, LUM, C163A and PTPRJ.

The biological sample includes, such as for example tissue, blood,plasma, serum, whole blood, urine, saliva, genital secretion,cerebrospinal fluid, sweat and excreta.

In one aspect, the determining the likelihood of cancer is determined bythe sensitivity, specificity, negative predictive value or positivepredictive value associated with the score. The score determined has anegative predictive value (NPV) at least about 80%.

The measuring step is performed by selected reaction monitoring massspectrometry, using a compound that specifically binds the protein beingdetected or a peptide transition. In one embodiment, the compound thatspecifically binds to the protein being measured is an antibody or anaptamer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a line graph showing area under the curve for a receivingoperating curve for 15 protein LC-SRM-MS panels.

FIG. 2 shows six line graphs each showing area under the curve for areceiving operating curve for 15 protein LC-SRM-MS panels for differentpatient populations and for subjects with large and small PN

FIG. 3 is a graph showing variability among three studies used toevaluate 15 protein panels.

FIG. 4 is a line graph showing area under the curve for a receivingoperating curve for a 15 protein LC-SRM-MS panel.

FIG. 5 shows three line graphs each showing area under the curve for areceiving operating curve for a 15 protein LC-SRM-MS panel for adifferent patient population.

FIG. 6 shows the results of a query of blood proteins used to identifylung cancer using the “Ingenuity” ® program.

FIG. 7 is a bar diagram showing Pearson correlations for peptides fromthe same peptide, from the same protein and from different proteins.

FIG. 8 is a graph showing performance of the classifier on the trainingsamples, validation samples and all samples combined.

FIG. 9 is a graph showing clinical and molecular factors.

FIG. 10 is a schematic showing the molecular network containing the 13classifier proteins (green), 5 transcription factors (blue) and thethree networks (orange lines) of lung cancer, response to oxidativestress and lung inflammation.

FIG. 11 is a graph depicting interpretation of classifier score in termsof risk.

FIG. 12 is a graph showing performance of the classifier on thediscovery samples (n=143) and validation samples (n=104). Negativepredictive value (NPV) and specificity (SPC) are presented in terms ofclassifier score. A cancer prevalence of 20% was assumed.

FIG. 13 is a graph showing multivariate analysis of clinical (smoking,nodule size) and molecular (classifier score) factors as they relate tocancer and benign samples (n=247) in the discovery and validationstudies. Smoking is measured by pack-years on the vertical. Nodule sizeis represented by circle diameter. A reference value of 0.43 ispresented to illustrate the discrimination between low numbers of cancersamples less than the reference value as compared to the high number ofcancer samples above the reference value.

FIG. 14 is a graph showing the 13 classifier proteins (green), 4transcription regulators (blue) and the three networks (orange lines) oflung cancer, oxidative stress response and lung inflammation. Allreferences are human UniProt identifiers.

FIG. 15 is a graph showing scattering plot of nodule size vs. classifierscore of all 247 patients, demonstrating the lack of correlation betweenthe two variables.

FIG. 16 is a diagram showing the Pearson correlations for peptides fromthe same peptide (blue), from the same protein (green) and fromdifferent proteins (red).

DETAILED DESCRIPTION

The disclosed invention derives from the surprising discovery, that inpatients presenting with pulmonary nodule(s), protein markers in theblood exist that specifically identify and classify lung cancer.Accordingly the invention provides unique advantages to the patientassociated with early detection of lung cancer in a patient, includingincreased life span, decreased morbidity and mortality, decreasedexposure to radiation during screening and repeat screenings and aminimally invasive diagnostic model. Importantly, the methods of theinvention allow for a patient to avoid invasive procedures.

The routine clinical use of chest computed tomography (CT) scansidentifies millions of pulmonary nodules annually, of which only a smallminority are malignant but contribute to the dismal 15% five-yearsurvival rate for patients diagnosed with non-small cell lung cancer(NSCLC). The early diagnosis of lung cancer in patients with pulmonarynodules is a top priority, as decision-making based on clinicalpresentation, in conjunction with current non-invasive diagnosticoptions such as chest CT and positron emission tomography (PET) scans,and other invasive alternatives, has not altered the clinical outcomesof patients with Stage I NSCLC. The subgroup of pulmonary nodulesbetween 8 mm and 20 mm in size is increasingly recognized as being“intermediate” relative to the lower rate of malignancies below 8 mm andthe higher rate of malignancies above 20 mm [9]. Invasive sampling ofthe lung nodule by biopsy using transthoracic needle aspiration orbronchoscopy may provide a cytopathologic diagnosis of NSCLC, but arealso associated with both false-negative and non-diagnostic results. Insummary, a key unmet clinical need for the management of pulmonarynodules is a non-invasive diagnostic test that discriminates betweenmalignant and benign processes in patients with indeterminate pulmonarynodules (IPNs), especially between 8 mm and 20 mm in size.

The clinical decision to be more or less aggressive in treatment isbased on risk factors, primarily nodule size, smoking history and age[9] in addition to imaging. As these are not conclusive, there is agreat need for a molecular-based blood test that would be bothnon-invasive and provide complementary information to risk factors andimaging.

Accordingly, these and related embodiments will find uses in screeningmethods for lung conditions, and particularly lung cancer diagnostics.More importantly, the invention finds use in determining the clinicalmanagement of a patient. That is, the method of invention is useful inruling in or ruling out a particular treatment protocol for anindividual subject.

Cancer biology requires a molecular strategy to address the unmetmedical need for an assessment of lung cancer risk. The field ofdiagnostic medicine has evolved with technology and assays that providesensitive mechanisms for detection of changes in proteins. The methodsdescribed herein use a LC-SRM-MS technology for measuring theconcentration of blood plasma proteins that are collectively changed inpatients with a malignant PN. This protein signature is indicative oflung cancer. LC-SRM-MS is one method that provides for bothquantification and identification of circulating proteins in plasma.Changes in protein expression levels, such as but not limited tosignaling factors, growth factors, cleaved surface proteins and secretedproteins, can be detected using such a sensitive technology to assaycancer. Presented herein is a blood-based classification test todetermine the likelihood that a patient presenting with a pulmonarynodule has a nodule that is benign or malignant. The present inventionpresents a classification algorithm that predicts the relativelikelihood of the PN being benign or malignant.

More broadly, it is demonstrated that there are many variations on thisinvention that are also diagnostic tests for the likelihood that a PN isbenign or malignant. These are variations on the panel of proteins,protein standards, measurement methodology and/or classificationalgorithm.

As disclosed herein, archival plasma samples from subjects presentingwith PNs were analyzed for differential protein expression by massspectrometry and the results were used to identify biomarker proteinsand panels of biomarker proteins that are differentially expressed inconjunction with various lung conditions (cancer vs. non-cancer).

In one aspect of the invention, one hundred and sixty three panels werediscovered that allow for the classification of PN as being benign ormalignant. These panels include those listed on Table 1. In someembodiments the panel according to the invention includes measuring 1,2, 3, 4, 5 or more proteins selected from ISLR, ALDOA, KIT, GRP78,AIFM1, CD14, COIA1, IBP3, TSP1, BGH3, TETN, FRI, LG3BP, GGH, PRDX1 orLRP1. In other embodiments the panel includes any panel or proteinexemplified on Table 1. For, example the panel includes ALDOA, GRP78,CD14, COIA1, IBP3, FRIL, LG3BP, and LRP1

TABLE 1 Number pAUC Proteins Identifier Proteins Factor ISLR ALDOA KITGRP78 AIFM1 CD14 COIA1 1 9 4.562 0 1 0 1 0 1 1 2 8 4.488 0 1 0 1 0 1 1 311 4.451 1 1 0 1 0 0 1 4 11 4.357 1 1 0 1 0 0 1 5 11 4.331 1 1 0 0 0 1 16 13 4.324 1 1 0 0 0 1 1 7 10 4.205 1 1 0 1 0 0 1 8 11 4.193 1 1 0 0 0 01 9 12 4.189 1 1 0 1 0 0 1 10 12 4.182 1 0 0 0 0 1 1 11 12 4.169 1 1 0 10 0 1 12 8 4.107 1 1 0 1 0 1 1 13 13 4.027 0 1 1 1 0 1 1 14 10 3.994 0 11 1 0 1 1 15 11 3.979 1 1 1 1 0 1 1 16 10 3.932 1 1 0 1 0 1 1 17 113.926 1 1 0 0 0 1 1 18 12 3.913 1 0 1 1 0 0 1 19 12 3.872 0 1 1 1 0 1 120 12 3.864 1 1 1 0 0 1 1 21 14 3.853 1 1 0 1 0 1 1 22 9 3.849 1 1 0 1 00 1 23 12 3.846 1 1 1 1 0 0 1 24 10 3.829 0 1 1 1 0 1 0 25 10 3.829 0 11 1 0 1 1 26 12 3.826 1 0 0 0 1 0 1 27 7 3.804 1 1 0 1 0 1 1 28 10 3.8020 1 0 1 0 1 1 29 10 3.787 0 1 0 1 0 1 0 30 9 3.779 1 1 0 1 0 1 1 31 113.774 0 1 0 1 0 1 1 32 8 3.759 1 1 0 0 0 0 1 33 13 3.758 1 1 0 0 0 1 134 11 3.757 1 1 0 1 0 0 0 35 12 3.754 0 1 1 1 0 1 1 36 10 3.750 1 1 0 10 1 1 37 11 3.747 0 1 1 1 0 1 1 38 12 3.744 1 0 1 1 0 0 1 39 11 3.742 11 0 1 0 1 1 40 9 3.740 1 1 0 1 0 1 1 41 12 3.740 1 1 1 1 0 1 1 42 123.739 1 1 0 1 0 1 1 43 9 3.734 1 1 0 0 0 0 1 44 12 3.730 1 1 0 1 0 0 145 11 3.725 0 1 1 1 0 1 1 46 12 3.717 0 1 0 0 1 1 1 47 9 3.713 0 1 0 1 01 1 48 9 3.713 1 1 1 1 0 1 1 49 10 3.709 0 1 0 1 0 1 1 50 11 3.709 1 1 01 0 1 1 51 11 3.701 0 1 1 1 1 1 1 52 12 3.685 1 1 0 1 0 1 1 53 10 3.6800 0 0 1 0 1 0 54 11 3.676 1 1 1 1 0 0 1 55 9 3.668 0 1 0 1 0 1 1 56 93.659 0 0 0 1 0 1 0 57 14 3.657 1 1 0 1 1 1 1 58 10 3.655 1 1 0 1 0 0 159 11 3.643 0 1 1 1 0 1 1 60 9 3.643 0 1 0 1 0 1 0 61 8 3.640 1 1 0 1 01 0 62 12 3.640 1 1 1 1 0 1 1 63 10 3.638 1 1 0 1 0 0 1 64 12 3.633 1 00 1 1 0 1 65 10 3.632 1 1 0 1 0 1 1 66 11 3.627 1 1 0 1 0 1 0 67 103.627 1 1 0 0 0 1 0 68 10 3.623 1 1 1 0 0 0 1 69 11 3.619 1 0 0 1 0 1 170 6 3.617 1 1 0 1 0 0 1 71 12 3.617 1 0 0 1 0 1 1 72 11 3.613 1 1 0 1 01 0 73 11 3.608 1 1 0 1 0 1 0 74 13 3.608 1 1 1 1 0 1 1 75 11 3.605 0 11 1 0 1 1 76 11 3.602 0 1 1 1 0 1 1 77 10 3.600 1 1 0 1 0 0 0 78 113.596 1 1 0 1 0 0 1 79 10 3.592 1 1 0 1 0 1 0 80 11 3.587 1 0 1 0 0 0 181 13 3.584 1 1 0 1 1 1 1 82 8 3.584 0 1 0 1 0 1 0 83 11 3.581 1 1 1 1 01 0 84 13 3.578 1 1 0 1 0 1 0 85 9 3.573 1 1 1 0 0 1 1 86 9 3.572 1 1 01 0 0 1 87 13 3.571 1 1 1 1 0 1 0 88 10 3.569 1 1 0 1 0 0 1 89 9 3.569 01 0 1 0 1 0 90 8 3.559 0 1 0 1 0 1 0 91 10 3.558 0 1 0 1 0 1 0 92 123.554 1 1 0 1 0 1 1 93 11 3.552 0 1 0 1 0 1 0 94 12 3.549 0 1 0 1 0 1 095 8 3.547 1 1 1 0 0 1 1 96 12 3.545 1 1 1 1 0 1 1 97 8 3.542 1 1 1 0 00 0 98 11 3.536 1 1 1 1 0 0 1 99 14 3.530 1 1 1 1 0 1 1 100 9 3.527 1 10 1 0 1 1 101 10 3.522 0 1 1 0 1 1 1 102 12 3.509 1 1 0 1 0 1 1 103 53.505 0 1 0 0 0 1 0 104 11 3.500 1 1 0 0 1 0 1 105 11 3.497 1 1 1 1 0 01 106 9 3.491 1 1 0 0 0 1 0 107 7 3.489 0 1 1 0 0 1 0 108 13 3.486 1 1 11 0 1 1 109 11 3.483 1 1 1 1 0 0 1 110 10 3.477 1 1 1 1 0 1 1 111 103.473 1 1 0 0 0 1 1 112 15 3.468 1 1 0 1 1 1 1 113 10 3.467 0 1 0 0 1 10 114 12 3.467 1 1 0 0 1 1 1 115 13 3.467 1 1 0 1 1 0 1 116 10 3.467 0 10 1 0 1 0 117 8 3.465 1 1 0 1 0 0 1 118 10 3.464 0 1 0 1 1 1 1 119 153.464 1 1 0 1 1 1 1 120 11 3.462 1 1 0 1 0 1 1 121 9 3.460 1 1 0 0 0 1 0122 13 3.453 1 1 0 1 0 1 1 123 12 3.449 1 1 1 0 0 1 0 124 10 3.448 1 1 01 0 1 0 125 10 3.445 0 1 1 1 0 1 0 126 6 3.441 0 1 0 0 0 1 0 127 113.440 1 1 0 1 0 1 0 128 12 3.440 1 1 0 1 1 0 0 129 11 3.439 1 1 0 1 0 10 130 10 3.426 0 1 0 0 1 1 0 131 11 3.423 1 1 0 0 0 0 1 132 10 3.420 1 10 0 0 1 0 133 10 3.419 1 1 1 1 0 1 0 134 11 3.417 1 1 0 1 1 0 1 135 123.414 0 1 0 1 1 1 1 136 10 3.413 0 1 1 1 0 1 0 137 11 3.400 0 1 0 0 1 10 138 12 3.398 1 1 0 1 0 1 0 139 13 3.396 1 1 0 1 0 1 0 140 9 3.386 1 10 0 0 1 0 141 9 3.373 1 1 0 1 0 1 0 142 12 3.363 1 1 0 0 1 0 1 143 83.362 0 1 0 1 0 1 0 144 10 3.360 1 1 0 1 0 1 1 145 9 3.359 1 1 1 0 0 1 0146 7 3.349 0 1 0 0 0 0 0 147 7 3.348 1 1 0 0 0 1 1 148 9 3.340 1 0 0 00 1 0 149 9 3.335 1 1 0 1 0 1 0 150 11 3.333 0 1 1 1 0 1 0 151 9 3.333 00 0 1 0 1 0 152 10 3.328 1 1 0 1 0 1 0 153 7 3.315 0 1 0 1 0 1 0 154 113.311 1 1 0 1 1 1 1 155 11 3.293 1 1 0 1 0 1 0 156 8 3.292 1 1 0 1 0 0 0157 9 3.289 0 1 0 1 0 1 0 158 7 3.229 0 1 0 0 0 1 0 159 7 3.229 1 1 0 00 1 0 160 7 3.203 1 1 0 1 0 0 0 161 12 3.161 1 1 1 0 1 1 0 162 9 3.138 11 0 0 1 0 1 163 13 3.078 1 1 0 0 1 0 1 Proteins Identifier IBP3 TSP1BGH3 TETN FRIL LG3BP GGH PRDX1 LRP1  1 1 0 0 0 1 1 0 0 1  2 1 0 0 0 1 10 0 1  3 1 1 1 1 1 0 0 1 1  4 1 1 0 0 1 1 1 1 1  5 0 1 1 1 1 0 1 1 1  61 1 1 1 1 1 1 1 1  7 0 1 1 1 1 0 0 1 1  8 0 1 1 1 1 0 1 1 1  9 1 1 1 1 10 0 1 1  10 1 1 1 1 1 1 0 1 1  11 1 1 0 0 1 1 1 1 1  12 0 0 0 0 1 1 0 01  13 1 1 0 0 1 1 1 1 1  14 1 0 0 0 1 1 0 0 1  15 0 0 0 0 1 1 1 0 1  160 0 0 1 1 1 0 0 1  17 1 1 1 1 1 0 0 1 1  18 1 1 0 0 1 1 1 1 1  19 1 0 00 1 1 1 1 1  20 0 1 1 1 1 1 0 1 1  21 1 1 1 1 1 1 0 1 1  22 0 1 1 1 1 00 0 1  23 1 1 0 0 1 1 1 1 1  24 1 0 0 0 1 1 1 1 1  25 1 0 0 0 1 1 1 0 1 26 1 1 1 1 1 0 1 1 1  27 0 0 0 0 0 1 0 0 1  28 1 0 0 0 1 1 1 1 1  29 11 0 0 1 1 1 1 1  30 0 0 0 0 1 1 0 0 1  31 1 0 0 0 1 1 1 1 1  32 0 0 1 11 0 0 1 1  33 1 1 1 1 1 1 0 1 1  34 1 1 1 1 1 1 0 1 1  35 1 1 0 0 1 1 11 1  36 1 0 0 0 1 1 0 1 1  37 1 1 0 0 1 1 1 1 0  38 1 1 1 1 1 0 0 1 1 39 1 1 0 1 1 1 0 0 1  40 1 0 0 0 1 1 0 0 1  41 1 0 0 1 1 1 0 0 1  42 11 0 0 1 1 1 1 1  43 0 1 1 1 1 0 0 1 1  44 1 1 1 1 1 1 0 1 1  45 1 0 0 11 1 0 0 1  46 1 1 1 1 1 1 1 1 0  47 1 0 0 0 1 1 0 1 1  48 0 0 0 0 1 1 00 1  49 1 0 0 0 1 1 1 0 1  50 0 1 1 1 1 1 0 0 1  51 1 0 0 0 1 1 0 0 1 52 1 1 1 1 1 1 0 0 1  53 1 1 1 1 1 1 0 1 1  54 0 1 1 1 1 0 0 1 1  55 10 0 0 1 1 1 0 1  56 1 1 0 0 1 1 1 1 0  57 1 1 1 1 1 0 0 1 1  58 0 1 0 01 1 1 0 1  59 1 0 0 0 1 1 1 1 1  60 1 0 1 0 1 1 0 0 1  61 1 0 0 0 1 1 00 1  62 0 0 0 1 1 1 0 1 1  63 0 1 1 1 1 1 0 0 1  64 1 1 1 1 1 0 0 1 1 65 1 0 0 0 1 1 0 0 1  66 1 1 1 1 1 1 0 0 1  67 1 1 1 1 1 1 0 0 1  68 01 1 1 1 1 0 0 1  69 1 1 1 0 1 1 0 0 1  70 0 0 0 0 0 1 0 0 1  71 1 1 1 11 0 0 1 1  72 1 1 0 0 1 1 1 1 1  73 1 1 1 0 1 1 0 1 1  74 1 1 0 0 1 1 01 1  75 1 0 0 0 1 1 0 1 1  76 1 0 0 0 1 1 1 0 1  77 1 1 1 1 1 1 0 1 0 78 1 1 1 1 1 0 1 0 1  79 1 1 0 0 1 1 0 1 1  80 1 1 1 1 0 1 0 1 1  81 11 1 1 1 1 0 0 1  82 1 1 0 0 1 1 0 1 0  83 1 1 0 0 1 1 1 1 0  84 1 1 1 11 1 0 1 1  85 1 0 0 0 1 1 0 0 0  86 0 1 0 0 1 1 0 0 1  87 1 1 0 0 1 1 11 1  88 1 1 0 1 1 0 0 1 1  89 1 1 0 0 1 1 0 1 1  90 1 0 0 0 1 1 0 0 1 91 1 0 0 1 1 1 1 1 1  92 0 1 1 1 1 0 1 1 1  93 1 1 0 0 1 1 1 1 1  94 11 1 1 1 1 1 1 1  95 1 1 0 0 0 1 0 0 0  96 1 0 0 0 1 1 1 0 1  97 1 1 0 10 1 0 0 0  98 1 0 0 0 1 1 1 1 1  99 1 1 0 1 1 1 1 1 0 100 0 1 0 0 1 1 00 1 101 1 1 0 0 1 1 0 1 0 102 0 0 1 1 1 1 0 1 1 103 1 1 0 0 0 1 0 0 0104 1 1 1 1 1 0 1 1 0 105 1 1 0 0 1 1 0 0 1 106 1 1 0 0 0 1 1 1 0 107 11 0 0 0 1 0 1 0 108 1 0 0 1 1 1 0 1 1 109 1 0 0 0 1 1 1 0 1 110 1 0 0 01 1 0 0 1 111 0 0 1 1 1 1 0 0 1 112 1 1 1 1 1 0 1 1 1 113 1 1 1 1 1 1 01 0 114 1 1 1 1 0 1 0 1 1 115 1 1 1 1 1 0 0 1 1 116 1 1 0 0 1 1 1 0 1117 0 1 0 0 1 1 0 0 1 118 1 0 0 0 1 1 0 0 1 119 1 1 1 1 1 1 1 1 0 120 00 0 1 1 1 0 1 1 121 1 1 1 1 0 1 0 1 0 122 1 1 1 1 1 1 1 1 0 123 1 1 0 11 1 1 1 0 124 1 1 0 0 1 1 1 1 0 125 1 1 0 0 1 1 0 1 1 126 1 1 0 0 0 1 00 0 127 1 1 0 0 1 1 1 0 1 128 1 1 1 1 1 0 0 1 1 129 1 0 0 0 1 1 1 1 1130 1 1 1 1 0 1 0 1 0 131 1 1 1 1 1 1 1 1 0 132 1 1 0 1 1 1 1 1 0 133 10 0 0 1 1 0 0 1 134 0 0 1 1 1 0 0 1 1 135 1 1 0 1 1 1 0 0 1 136 1 1 0 01 1 0 1 0 137 1 1 1 1 1 1 0 1 0 138 1 0 1 1 1 1 1 1 1 139 1 1 1 1 1 1 11 1 140 1 1 0 0 1 1 1 1 0 141 1 0 0 0 1 1 0 0 1 142 1 1 1 1 1 1 1 1 0143 1 0 0 0 1 1 0 1 1 144 0 0 0 1 1 1 0 1 0 145 1 1 0 0 1 1 0 0 0 146 11 1 1 0 1 0 0 0 147 1 1 0 0 0 1 0 0 0 148 1 1 1 1 0 1 0 1 0 149 1 1 0 01 1 0 0 1 150 1 1 0 0 1 1 0 1 1 151 1 1 1 0 1 1 0 0 1 152 1 0 0 0 1 1 10 1 153 1 0 0 0 1 1 0 0 1 154 0 0 0 1 1 1 1 0 0 155 1 0 1 0 1 1 0 1 1156 1 1 0 0 1 1 0 0 1 157 1 1 0 0 1 1 0 1 0 158 1 1 0 0 1 1 0 0 0 159 11 0 0 0 1 0 1 0 160 1 0 0 0 1 1 0 0 1 161 1 1 1 1 1 1 0 1 0 162 0 0 1 11 1 0 0 0 163 1 1 1 1 1 1 1 1 0 1 = in the panel; 0 = not in the panel.

The one hundred best random panels of proteins out of the milliongenerated are shown in Table 2.

TABLE 2 Protein 1 Protein 2 Protein 3 Protein 4 Protein 5 Protein 6Protein 7 Protein 8 Protein 9 Protein 10 1 IBP3 TSP1 CO6A3 PDIA3 SEM3GSAA 6PGD EF1A1 PRDX1 TERA 2 EPHB6 CNTN1 CLUS IBP3 BGH3 6PGD FRIL LRP1TBB3 ERO1A 3 PPIB LG3BP MDHC DSG2 BST1 CD14 DESP PRDX1 CDCP1 MMP9 4 TPISCOIA1 IBP3 GGH ISLR MMP2 AIFM1 DSG2 1433T CBPB2 5 TPIS IBP3 CH10 SEM3G6PGD FRIL ICAM3 TERA FINC ERO1A 6 BGH3 ICAM1 MMP12 6PGD CD14 EF1A1 HYOU1PLXC1 PROF1 ERO1A 7 KIT LG3BP TPIS IBP3 LDHB GGH TCPA ISLR CBPB2 EF1A1 8LG3BP IBP3 LDHB TSP1 CRP ZA2G CD14 LRP1 PLIN2 ERO1A 9 COIA1 TSP1 ISLRTFR1 CBPB2 FRIL LRP1 UGPA PTPA ERO1A 10 CO6A3 SEM3G APOE FRIL ICAM3PRDX1 EF2 HS90B NCF4 PTPA 11 PPIB LG3BP COIA1 APOA1 DSG2 APOE CD14 PLXC1NCF4 GSLG1 12 SODM EPHB6 C163A COIA1 LDHB TETN 1433T CD14 PTPA ERO1A 13SODM KPYM IBP3 TSP1 BGH3 SEM3G 6PGD CD14 RAP2B EREG 14 EPHB6 ALDOA MMP7COIA1 TIMP1 GRP78 MMP12 CBPB2 G3P PTPA 15 KIT TSP1 SCF TIMP1 OSTP PDIA3GRP78 TNF12 PRDX1 PTPA 16 IBP2 LG3BP GELS HPT FIBA GGH ICAM1 BST1 HYOU1GSLG1 17 KIT CD44 CH10 PEDF ICAM1 6PGD S10A1 ERO1A GSTP1 MMP9 18 LG3BPC163A GGH ERBB3 TETN BGH3 ENOA GDIR2 LRP1 ERO1A 19 SODM KPYM BGH3 FOLH16PGD DESP LRP1 TBA1B ERO1A GSTP1 20 CNTN1 TETN ICAM1 K1C19 ZA2G 6PGD EF2RAN ERO1A GSTP1 21 GELS ENPL OSTP PEDF ICAM1 BST1 TNF12 GDIR2 LRP1 ERO1A22 KIT LDHA IBP3 PEDF DSG2 FOLH1 CD14 LRP1 UGPA ERO1A 23 KIT TSP1 ISLRBGH3 COF1 PTPRJ 6PGD LRP1 S10A6 MPRI 24 LG3BP C163A GGH DSG2 ICAM1 6PGDGDIR2 HYOU1 EREG ERO1A 25 IBP2 C163A ENPL FIBA BGH3 CERU 6PGD LRP1 PRDX1MMP9 26 LG3BP C163A TENX PDIA3 SEM3G BST1 VTNC FRIL PRDX1 ERO1A 27 ALDOACOIA1 TETN 1433T CBPB2 CD14 G3P CD59 ERO1A MMP9 28 IBP3 TENX CRP TETNMMP2 SEM3G VTNC CD14 PROF1 ERO1A 29 SODM EPHB6 TPIS TENX ERBB3 SCF TETNFRIL LRP1 ERO1A 30 LG3BP IBP3 POSTN DSG2 MDHM 1433Z CD14 EF1A1 PLXC1ERO1A 31 IBP2 LG3BP COIA1 CNTN1 IBP3 POSTN TETN BGH3 6PGD ERO1A 32 PVRTSP1 GGH CYTB AIFM1 ICAM1 MDHM 1433Z 6PGD FRIL 33 LYOX GELS COIA1 IBP3AIFM1 ICAM1 FRIL PRDX1 RAP2B NCF4 34 KIT AMPN TETN TNF12 6PGD FRIL LRP1EF2 ERO1A MMP9 35 LG3BP GELS COIA1 CLUS CALU AIFM1 1433T CD14 UGPA S10A136 ALDOA IBP3 TSP1 TETN SEM3G ICAM1 EF1A1 G3P RAP2B NCF4 37 ALDOA COIA1CH10 TETN PTPRJ SEM3G 1433T 6PGD FRIL ERO1A 38 LG3BP COIA1 PLSL FIBATENX POSTN CD14 LRP1 NCF4 ERO1A 39 LUM IBP3 CH10 AIFM1 MDHM 6PGD PLXC1EF2 CD59 GSTP1 40 SODM LG3BP LUM LDHA MDHC GGH ICAM1 LRP1 TBA1B ERO1A 41LG3BP CD44 IBP3 CALU CERU 1433T CD14 CLIC1 NCF4 ERO1A 42 LG3BP TPISCOIA1 HPT FIBA AIFM1 1433Z 6PGD CD14 EF2 43 ALDOA CD44 MMP2 CD14 FRILPRDX1 RAN NCF4 MPRI PTPA 44 COIA1 CLUS OSTP ICAM1 1433T PLXC1 PTGISRAP2B PTPA GSTP1 45 KIT LYOX IBP3 GRP78 FOLH1 MASP1 CD14 LRP1 ERO1AGSTP1 46 LG3BP GGH CRP SCF ICAM1 ZA2G 1433T RAN NCF4 ERO1A 47 LG3BPC163A BGH3 MMP2 GRP78 LRP1 RAN ITA5 HS90B PTPA 48 ALDOA CLUS TENX ICAM1K1C19 MASP1 6PGD CBPB2 PRDX1 PTPA 49 IBP3 PDIA3 PEDF FOLH1 ICAM1 NRP16PGD UGPA RAN ERO1A 50 ENPL FIBA ISLR SAA 6PGD PRDX1 EF2 PLIN2 HS90BGSLG1 51 LG3BP COIA1 CO6A3 GGH ERBB3 FOLH1 ICAM1 RAN CDCP1 ERO1A 52 GELSENPL A1AG1 SCF COF1 ICAM1 6PGD RAP2B EF2 HS90B 53 SODM IBP2 COIA1 CLUSIBP3 ENPL PLSL TNF12 6PGD ERO1A 54 KIT MMP7 COIA1 TSP1 CO6A3 GGH PDIA3ICAM1 LRP1 GSLG1 55 ALDOA COIA1 TSP1 CH10 NRP1 CD14 DESP LRP1 CLIC1ERO1A 56 C163A GELS CALU A1AG1 AIFM1 DSG2 ICAM1 6PGD RAP2B NCF4 57 PPIBLG3BP IBP3 TSP1 PLSL GRP78 FOLH1 6PGD HYOU1 RAP2B 58 KIT LG3BP LUM GELSOSTP ICAM1 CD14 EF1A1 NCF4 MMP9 59 KIT PPIB LG3BP GELS FOLH1 ICAM1 MASP1GDIR2 ITA5 NCF4 60 IBP3 ENPL ERBB3 BGH3 VTNC 6PGD EF1A1 TBA1B S10A6HS90B 61 LG3BP CLUS IBP3 SCF TCPA ISLR GRP78 6PGD ERO1A GSTP1 62 LG3BPLEG1 GELS GGH TETN ENOA ICAM1 MASP1 FRIL NCF4 63 LG3BP CD44 TETN BGH3G3P LRP1 PRDX1 CDCP1 PTPA MMP9 64 CALU ENPL ICAM1 VTNC FRIL LRP1 PROF1TBB3 GSLG1 ERO1A 65 PPIB PLSL TENX A1AG1 COF1 6PGD FRIL LRP1 CLIC1 ERO1A66 IBP2 IBP3 CERU ENOA 6PGD CD14 LRP1 PDGFB ERO1A GSTP1 67 COIA1 1433TCD14 DESP GDIR2 PLXC1 PROF1 RAP2B RAN ERO1A 68 LYOX OSTP TETN SEM3GICAM1 ZA2G FRIL EREG RAN ERO1A 69 LG3BP IBP3 TSP1 PEDF FOLH1 MDHM TNF12NRP1 S10A6 RAP2B 70 KIT ALDOA LG3BP COIA1 TSP1 A1AG1 BGH3 SEM3G FOLH1RAN 71 ALDOA OSTP BST1 CD14 G3P PRDX1 PTGIS FINC PTPA MMP9 72 EPHB6 TETNPEDF ICAM1 APOE PROF1 UGPA NCF4 GSLG1 PTPA 73 LG3BP COIA1 ENPL MMP21433T EF1A1 LRP1 HS90B GSLG1 ERO1A 74 KIT IBP3 CYTB MMP2 1433Z 6PGDCLIC1 EF2 NCF4 PTPA 75 SODM LYOX IBP3 TETN SEM3G CD14 PRDX1 PTPA ERO1AGSTP1 76 SODM KPYM COIA1 MDHC TCPA CD14 FRIL LRP1 EF2 ERO1A 77 PPIBLG3BP FIBA GRP78 AIFM1 ICAM1 6PGD NCF4 GSLG1 PTPA 78 LG3BP C163A PVRMDHC TETN SEM3G AIFM1 6PGD EREG ERO1A 79 GELS ISLR BGH3 DSG2 ICAM1 SAAHYOU1 ICAM3 PTGIS RAP2B 80 KPYM TPIS IBP3 TIMP1 GRP78 ICAM1 LRP1 TERAERO1A MMP9 81 IBP3 HPT TSP1 GRP78 SAA MMP12 1433Z 6PGD CD14 S10A6 82TENX A1AG1 ENOA AIFM1 6PGD CD14 FRIL LRP1 RAP2B CD59 83 ALDOA KPYM ISLRTETN BGH3 VTNC LRP1 ITA5 PTPA MMP9 84 SODM TENX ISLR TETN VTNC 6PGD LRP1EF2 ERO1A MMP9 85 LG3BP C163A COIA1 FOLH1 CD14 LRP1 TBA1B GSLG1 ERO1AGSTP1 86 SODM PVR COIA1 ISLR PDIA3 APOE CD14 FRIL LRP1 CDCP1 87 ALDOAPEDF ICAM1 6PGD CD14 FINC RAN NCF4 GSLG1 PTPA 88 LG3BP KPYM GELS COIA1IBP3 CD14 EF1A1 PLIN2 HS90B ERO1A 89 LG3BP PVR CLUS TETN COF1 SEM3G DESPEF2 HS90B ERO1A 90 LG3BP COIA1 FIBA TETN TFR1 ICAM1 MDHM CD14 PLXC1ERO1A 91 PPIB LG3BP GELS CLUS TENX ICAM1 SAA NCF4 PTPA ERO1A 92 COIA1TSP1 ISLR BGH3 SAA 6PGD LRP1 PROF1 EREG ERO1A 93 CALU FIBA OSTP ISLRPDIA3 SEM3G K1C19 6PGD HYOU1 RAP2B 94 FIBA CH10 GRP78 SEM3G AIFM1 ICAM1MDHM FRIL UGPA GSTP1 95 COIA1 IBP3 PDIA3 ICAM1 K1C19 CD14 EF1A1 FRILPTGIS PDGFB 96 LG3BP C163A COIA1 LDHA 1433T 1433Z FRIL LRP1 ERO1A MMP997 LG3BP GELS COIA1 GRP78 SEM3G FRIL PLXC1 PROF1 S10A1 ERO1A 98 LG3BPCOIA1 ENPL GRP78 AIFM1 ICAM1 1433Z CD14 LRP1 ERO1A 99 COIA1 PLSL NRP11433T CD14 FRIL LRP1 RAP2B PDGFB ERO1A 100 IBP2 COIA1 TETN DSG2 FOLH11433T CD14 FRIL LRP1 ERO1APreferred panels for ruling in treatment for a subject include thepanels listed on Table 3 and 4. In various other embodiments, the panelsaccording to the invention include measuring at least 2, 3, 4, 5, 6, 7,or more of the proteins listed on Tables 2 and 3.

TABLE 3 Average (19) Rule-out (20) Rule-in (16) ERO1A ERO1A ERO1A 6PGD6PGD 6PGD FRIL FRIL FRIL GSTP1 GSTP1 GSTP1 COIA1 COIA1 COIA1 GGH GGH GGHPRDX1 PRDX1 PRDX1 LRP1 CD14 SEM3G ICAM1 LRP1 GRP78 CD14 LG3BP TETN LG3BPPTPA AIFM1 PTPA ICAM1 TSP1 TETN TSP1 MPRI GRP78 IBP3 TNF12 AIFM1 FOLH1MMP9 SEM3G SODM OSTP BGH3 FIBA PDIA3 GSLG1 FINC RAP2B C163A

TABLE 4 Average (13) Rule-out (13) Rule-in (9) LRP1 LRP1 ( LRP1 BGH3COIA1 COIA1 COIA1 TETN TETN TETN TSP1 TSP1 TSP1 ALDOA ALDOA PRDX1 GRP78GRP78 PROF1 FRIL FRIL GRP78 LG3BP APOE FRIL BGH3 TBB3 LG3BP ISLR CD14PRDX1 GGH FIBA AIFM1 GSLG1A preferred normalizer panel is listed in Table 5.

TABLE 5 Normalizer (6) PEDF MASP1 GELS LUM C163A PTPRJ

The term “pulmonary nodules” (PNs) refers to lung lesions that can bevisualized by radiographic techniques. A pulmonary nodule is any nodulesless than or equal to three centimeters in diameter. In one example apulmonary nodule has a diameter of about 0.8 cm to 2 cm.

The term “masses” or “pulmonary masses” refers to lung nodules that aregreater than three centimeters maximal diameter.

The term “blood biopsy” refers to a diagnostic study of the blood todetermine whether a patient presenting with a nodule has a conditionthat may be classified as either benign or malignant.

The term “acceptance criteria” refers to the set of criteria to which anassay, test, diagnostic or product should conform to be consideredacceptable for its intended use. As used herein, acceptance criteria area list of tests, references to analytical procedures, and appropriatemeasures, which are defined for an assay or product that will be used ina diagnostic. For example, the acceptance criteria for the classifierrefers to a set of predetermined ranges of coefficients.

The term “average maximal AUC” refers to the methodology of calculatingperformance. For the present invention, in the process of defining theset of proteins that should be in a panel by forward or backwardsselection proteins are removed or added one at a time. A plot can begenerated with performance (AUC or partial AUC score on the Y axis andproteins on the X axis) the point which maximizes performance indicatesthe number and set of proteins the gives the best result.

The term “partial AUC factor or pAUC factor” is greater than expected byrandom prediction. At sensitivity=0.90 the pAUC factor is thetrapezoidal area under the ROC curve from 0.9 to 1.0Specificity/(0.1*0.1/2).

The term “incremental information” refers to information that may beused with other diagnostic information to enhance diagnostic accuracy.Incremental information is independent of clinical factors such asincluding nodule size, age, or gender.

The term “score” or “scoring” refers to the refers to calculating aprobability likelihood for a sample. For the present invention, valuescloser to 1.0 are used to represent the likelihood that a sample iscancer, values closer to 0.0 represent the likelihood that a sample isbenign.

The term “robust” refers to a test or procedure that is not seriouslydisturbed by violations of the assumptions on which it is based. For thepresent invention, a robust test is a test wherein the proteins ortransitions of the mass spectrometry chromatograms have been manuallyreviewed and are “generally” free of interfering signals

The term “coefficients” refers to the weight assigned to each proteinused to in the logistic regression equation to score a sample.

In certain embodiments of the invention, it is contemplated that interms of the logistic regression model of MC CV, the model coefficientand the coefficient of variation (CV) of each protein's modelcoefficient may increase or decrease, dependent upon the method (ormodel) of measurement of the protein classifier. For each of the listedproteins in the panels, there is about, at least, at least about, or atmost about a 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9-, or 10-, -fold or any rangederivable therein for each of the coefficient and CV. Alternatively, itis contemplated that quantitative embodiments of the invention may bediscussed in terms of as about, at least, at least about, or at mostabout 10, 20, 30, 40, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,98, 99% or more, or any range derivable therein.

The term “best team players” refers to the proteins that rank the bestin the random panel selection algorithm, i.e., perform well on panels.When combined into a classifier these proteins can segregate cancer frombenign samples. “Best team player” proteins is synonymous with“cooperative proteins”. The term “cooperative proteins” refers proteinsthat appear more frequently on high performing panels of proteins thanexpected by chance. This gives rise to a protein's cooperative scorewhich measures how (in)frequently it appears on high performing panels.For example, a protein with a cooperative score of 1.5 appears on highperforming panels 1.5× more than would be expected by chance alone.

The term “classifying” as used herein with regard to a lung conditionrefers to the act of compiling and analyzing expression data for usingstatistical techniques to provide a classification to aid in diagnosisof a lung condition, particularly lung cancer.

The term “classifier” as used herein refers to an algorithm thatdiscriminates between disease states with a predetermined level ofstatistical significance. A two-class classifier is an algorithm thatuses data points from measurements from a sample and classifies the datainto one of two groups. In certain embodiments, the data used in theclassifier is the relative expression of proteins in a biologicalsample. Protein expression levels in a subject can be compared to levelsin patients previously diagnosed as disease free or with a specifiedcondition.

The “classifier” maximizes the probability of distinguishing a randomlyselected cancer sample from a randomly selected benign sample, i.e., theAUC of ROC curve.

In addition to the classifier's constituent proteins with differentialexpression, it may also include proteins with minimal or no biologicvariation to enable assessment of variability, or the lack thereof,within or between clinical specimens; these proteins may be termedendogenous proteins and serve as internal controls for the otherclassifier proteins.

The term “normalization” or “normalizer” as used herein refers to theexpression of a differential value in terms of a standard value toadjust for effects which arise from technical variation due to samplehandling, sample preparation and mass spectrometry measurement ratherthan biological variation of protein concentration in a sample. Forexample, when measuring the expression of a differentially expressedprotein, the absolute value for the expression of the protein can beexpressed in terms of an absolute value for the expression of a standardprotein that is substantially constant in expression. This prevents thetechnical variation of sample preparation and mass spectrometrymeasurement from impeding the measurement of protein concentrationlevels in the sample.

The term “condition” as used herein refers generally to a disease,event, or change in health status.

The term “treatment protocol” as used herein including furtherdiagnostic testing typically performed to determine whether a pulmonarynodule is benign or malignant. Treatment protocols include diagnostictests typically used to diagnose pulmonary nodules or masses such as forexample, CT scan, positron emission tomography (PET) scan, bronchoscopyor tissue biopsy. Treatment protocol as used herein is also meant toinclude therapeutic treatments typically used to treat malignantpulmonary nodules and/or lung cancer such as for example, chemotherapy,radiation or surgery.

The terms “diagnosis” and “diagnostics” also encompass the terms“prognosis” and “prognostics”, respectively, as well as the applicationsof such procedures over two or more time points to monitor the diagnosisand/or prognosis over time, and statistical modeling based thereupon.Furthermore the term diagnosis includes: a. prediction (determining if apatient will likely develop a hyperproliferative disease) b. prognosis(predicting whether a patient will likely have a better or worse outcomeat a pre-selected time in the future) c. therapy selection d.therapeutic drug monitoring e. relapse monitoring.

In some embodiments, for example, classification of a biological sampleas being derived from a subject with a lung condition may refer to theresults and related reports generated by a laboratory, while diagnosismay refer to the act of a medical professional in using theclassification to identify or verify the lung condition.

The term “providing” as used herein with regard to a biological samplerefers to directly or indirectly obtaining the biological sample from asubject. For example, “providing” may refer to the act of directlyobtaining the biological sample from a subject (e.g., by a blood draw,tissue biopsy, lavage and the like). Likewise, “providing” may refer tothe act of indirectly obtaining the biological sample. For example,providing may refer to the act of a laboratory receiving the sample fromthe party that directly obtained the sample, or to the act of obtainingthe sample from an archive.

As used herein, “lung cancer” preferably refers to cancers of the lung,but may include any disease or other disorder of the respiratory systemof a human or other mammal. Respiratory neoplastic disorders include,for example small cell carcinoma or small cell lung cancer (SCLC),non-small cell carcinoma or non-small cell lung cancer (NSCLC), squamouscell carcinoma, adenocarcinoma, broncho-alveolar carcinoma, mixedpulmonary carcinoma, malignant pleural mesothelioma, undifferentiatedlarge cell carcinoma, giant cell carcinoma, synchronous tumors, largecell neuroendocrine carcinoma, adenosquamous carcinoma, undifferentiatedcarcinoma; and small cell carcinoma, including oat cell cancer, mixedsmall cell/large cell carcinoma, and combined small cell carcinoma; aswell as adenoid cystic carcinoma, hamartomas, mucoepidermoid tumors,typical carcinoid lung tumors, atypical carcinoid lung tumors,peripheral carcinoid lung tumors, central carcinoid lung tumors, pleuralmesotheliomas, and undifferentiated pulmonary carcinoma and cancers thatoriginate outside the lungs such as secondary cancers that havemetastasized to the lungs from other parts of the body. Lung cancers maybe of any stage or grade. Preferably the term may be used to refercollectively to any dysplasia, hyperplasia, neoplasia, or metastasis inwhich the protein biomarkers expressed above normal levels as may bedetermined, for example, by comparison to adjacent healthy tissue.

Examples of non-cancerous lung condition include chronic obstructivepulmonary disease (COPD), benign tumors or masses of cells (e.g.,hamartoma, fibroma, neurofibroma), granuloma, sarcoidosis, andinfections caused by bacterial (e.g., tuberculosis) or fungal (e.g.histoplasmosis) pathogens. In certain embodiments, a lung condition maybe associated with the appearance of radiographic PNs.

As used herein, “lung tissue”, and “lung cancer” refer to tissue orcancer, respectively, of the lungs themselves, as well as the tissueadjacent to and/or within the strata underlying the lungs and supportingstructures such as the pleura, intercostal muscles, ribs, and otherelements of the respiratory system. The respiratory system itself istaken in this context as representing nasal cavity, sinuses, pharynx,larynx, trachea, bronchi, lungs, lung lobes, aveoli, aveolar ducts,aveolar sacs, aveolar capillaries, bronchioles, respiratory bronchioles,visceral pleura, parietal pleura, pleural cavity, diaphragm, epiglottis,adenoids, tonsils, mouth and tongue, and the like. The tissue or cancermay be from a mammal and is preferably from a human, although monkeys,apes, cats, dogs, cows, horses and rabbits are within the scope of thepresent invention. The term “lung condition” as used herein refers to adisease, event, or change in health status relating to the lung,including for example lung cancer and various non-cancerous conditions.

“Accuracy” refers to the degree of conformity of a measured orcalculated quantity (a test reported value) to its actual (or true)value. Clinical accuracy relates to the proportion of true outcomes(true positives (TP) or true negatives (TN) versus misclassifiedoutcomes (false positives (FP) or false negatives (FN)), and may bestated as a sensitivity, specificity, positive predictive values (PPV)or negative predictive values (NPV), or as a likelihood, odds ratio,among other measures.

The term “biological sample” as used herein refers to any sample ofbiological origin potentially containing one or more biomarker proteins.Examples of biological samples include tissue, organs, or bodily fluidssuch as whole blood, plasma, serum, tissue, lavage or any other specimenused for detection of disease.

The term “subject” as used herein refers to a mammal, preferably ahuman.

The term “biomarker protein” as used herein refers to a polypeptide in abiological sample from a subject with a lung condition versus abiological sample from a control subject. A biomarker protein includesnot only the polypeptide itself, but also minor variations thereof,including for example one or more amino acid substitutions ormodifications such as glycosylation or phosphorylation.

The term “biomarker protein panel” as used herein refers to a pluralityof biomarker proteins. In certain embodiments, the expression levels ofthe proteins in the panels can be correlated with the existence of alung condition in a subject. In certain embodiments, biomarker proteinpanels comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 60, 70, 80,90 or 100 proteins. In certain embodiments, the biomarker proteinspanels comprise from 100-125 proteins, 125-150 proteins, 150-200proteins or more.

“Treating” or “treatment” as used herein with regard to a condition mayrefer to preventing the condition, slowing the onset or rate ofdevelopment of the condition, reducing the risk of developing thecondition, preventing or delaying the development of symptoms associatedwith the condition, reducing or ending symptoms associated with thecondition, generating a complete or partial regression of the condition,or some combination thereof.

The term “ruling out” as used herein is meant that the subject isselected not to receive a treatment protocol.

The term “ruling-in” as used herein is meant that the subject isselected to receive a treatment protocol.

Biomarker levels may change due to treatment of the disease. The changesin biomarker levels may be measured by the present invention. Changes inbiomarker levels may be used to monitor the progression of disease ortherapy.

“Altered”, “changed” or “significantly different” refer to a detectablechange or difference from a reasonably comparable state, profile,measurement, or the like. One skilled in the art should be able todetermine a reasonable measurable change. Such changes may be all ornone. They may be incremental and need not be linear. They may be byorders of magnitude. A change may be an increase or decrease by 1%, 5%,10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%, or more, orany value in between 0% and 100%. Alternatively the change may be1-fold, 1.5-fold 2-fold, 3-fold, 4-fold, 5-fold or more, or any valuesin between 1-fold and five-fold. The change may be statisticallysignificant with a p value of 0.1, 0.05, 0.001, or 0.0001.

Using the methods of the current invention, a clinical assessment of apatient is first performed. If there exists is a higher likelihood forcancer, the clinician may rule in the disease which will require thepursuit of diagnostic testing options yielding data which increaseand/or substantiate the likelihood of the diagnosis. “Rule in” of adisease requires a test with a high specificity.

“FN” is false negative, which for a disease state test means classifyinga disease subject incorrectly as non-disease or normal.

“FP” is false positive, which for a disease state test means classifyinga normal subject incorrectly as having disease.

The term “rule in” refers to a diagnostic test with high specificitythat coupled with a clinical assessment indicates a higher likelihoodfor cancer. If the clinical assessment is a lower likelihood for cancer,the clinician may adopt a stance to rule out the disease, which willrequire diagnostic tests which yield data that decrease the likelihoodof the diagnosis. “Rule out” requires a test with a high sensitivity.

The term “rule out” refers to a diagnostic test with high sensitivitythat coupled with a clinical assessment indicates a lower likelihood forcancer.

The term “sensitivity of a test” refers to the probability that apatient with the disease will have a positive test result. This isderived from the number of patients with the disease who have a positivetest result (true positive) divided by the total number of patients withthe disease, including those with true positive results and thosepatients with the disease who have a negative result, i.e. falsenegative.

The term “specificity of a test” refers to the probability that apatient without the disease will have a negative test result. This isderived from the number of patients without the disease who have anegative test result (true negative) divided by all patients without thedisease, including those with a true negative result and those patientswithout the disease who have a positive test result, e.g. falsepositive. While the sensitivity, specificity, true or false positiverate, and true or false negative rate of a test provide an indication ofa test's performance, e.g. relative to other tests, to make a clinicaldecision for an individual patient based on the test's result, theclinician requires performance parameters of the test with respect to agiven population.

The term “positive predictive value” (PPV) refers to the probabilitythat a positive result correctly identifies a patient who has thedisease, which is the number of true positives divided by the sum oftrue positives and false positives.

The term “negative predictive value” or “NPV” is calculated byTN/(TN+FN) or the true negative fraction of all negative test results.It also is inherently impacted by the prevalence of the disease andpre-test probability of the population intended to be tested.

The term “disease prevalence” refers to the number of all new and oldcases of a disease or occurrences of an event during a particularperiod. Prevalence is expressed as a ratio in which the number of eventsis the numerator and the population at risk is the denominator.

The term disease incidence refers to a measure of the risk of developingsome new condition within a specified period of time; the number of newcases during some time period, it is better expressed as a proportion ora rate with a denominator.

Lung cancer risk according to the “National Lung Screening Trial” isclassified by age and smoking history. High risk—age ≧55 and ≧30pack-years smoking history; Moderate risk—age ≧50 and ≧20 pack-yearssmoking history; Low risk—<age 50 or <20 pack-years smoking history.

The term “negative predictive value” (NPV) refers to the probabilitythat a negative test correctly identifies a patient without the disease,which is the number of true negatives divided by the sum of truenegatives and false negatives. A positive result from a test with asufficient PPV can be used to rule in the disease for a patient, while anegative result from a test with a sufficient NPV can be used to ruleout the disease, if the disease prevalence for the given population, ofwhich the patient can be considered a part, is known.

The clinician must decide on using a diagnostic test based on itsintrinsic performance parameters, including sensitivity and specificity,and on its extrinsic performance parameters, such as positive predictivevalue and negative predictive value, which depend upon the disease'sprevalence in a given population.

Additional parameters which may influence clinical assessment of diseaselikelihood include the prior frequency and closeness of a patient to aknown agent, e.g. exposure risk, that directly or indirectly isassociated with disease causation, e.g. second hand smoke, radiation,etc., and also the radiographic appearance or characterization of thepulmonary nodule exclusive of size. A nodule's description may includesolid, semi-solid or ground glass which characterizes it based on thespectrum of relative gray scale density employed by the CT scantechnology.

“Mass spectrometry” refers to a method comprising employing anionization source to generate gas phase ions from an analyte presentedon a sample presenting surface of a probe and detecting the gas phaseions with a mass spectrometer.

The technology liquid chromatography selected reaction monitoring massspectrometry (LC-SRM-MS) was used to assay the expression levels of acohort of 388 proteins in the blood to identify differences forindividual proteins which may correlate with the absence or presence ofthe disease. The individual proteins have not only been implicated inlung cancer biology, but are also likely to be present in plasma basedon their expression as membrane-anchored or secreted proteins. Ananalysis of epithelial and endothelial membranes of resected lung cancertissues (including the subtypes of adenocarcinoma, squamous, and largecell) identified 217 tissue proteins. A review of the scientificliterature with search terms relevant to lung cancer biology identified319 proteins. There was an overlap of 148 proteins between proteinsidentified by cancer tissue analysis or literature review, yielding atotal of 388 unique proteins as candidates. The majority of candidateproteins included in the multiplex LC-SRM-MS assay were discoveredfollowing proteomics analysis of secretory vesicle contents from freshNSCLC resections and from adjacent non-malignant tissue. The secretoryproteins reproducibly upregulated in the tumor tissue were identifiedand prioritized for inclusion in the LC-SRM-MS assay using extensivebioinformatic and literature annotation. An additional set of proteinsthat were present in relevant literature was also added to the assay. Intotal, 388 proteins associated with lung cancer were prioritized for SRMassay development. Of these, 371 candidate protein biomarkers wereultimately included in the assay. These are listed in Table 6, below.

TABLE 6 Subcellular Evidence for UniProt Protein Gene Sources ofBiomarkers Location Presence in Protein Name Symbol Tissue Biomarkers inLiterature (UniProt) Blood 1433B_HUMAN 14-3-3 YWHAB Secreted,LungCancers Cytoplasm. Literature, protein EPI Melanosome. Detectionbeta/alpha Note = Identified by mass spectrometry in melanosomefractions from stage I to stage IV. 1433E_HUMAN 14-3-3 YWHAE ENDOLungCancers, Cytoplasm Literature, protein Benign- (By similarity).Detection epsilon Nodules Melanosome. Note = Identified by massspectrometry in melanosome fractions from stage I to stage IV.1433S_HUMAN 14-3-3 SFN Secreted, LungCancers Cytoplasm. UniProt,Literature, protein EPI Nucleus (By Detection sigma similarity).Secreted. Note = May be secreted by a non- classical secretory pathway.1433T_HUMAN 14-3-3 YWHAQ EPI LungCancers, Cytoplasm. Detection proteinBenign- Note = In theta Nodules neurons, axonally transported to thenerve terminals. 1433Z_HUMAN 14-3-3 YWHAZ EPI LungCancers, Cytoplasm.Detection protein Benign- Melanosome. zeta/delta Nodules Note = Locatedto stage I to stage IV melanosomes. 6PGD_HUMAN 6- PGD EPI, ENDOCytoplasm Detection phosphogluconate (By similarity). dehydrogenase,decarboxylating A1AG1_HUMAN Alpha-1- ORM1 EPI Symptoms Secreted.UniProt, Literature, acid glycoprotein 1 Detection, PredictionABCD1_HUMAN ATP- ABCD1 ENDO Peroxisome Detection, binding membrane;Prediction cassette Multi-pass sub- membrane family D protein. member 1ADA12_HUMAN Disintegrin ADAM12 LungCancers, Isoform 1: UniProt,Detection, and Benign- Cell membrane; Prediction metallo- Nodules,Single- proteinase Symptoms pass domain- type I membrane containingprotein. protein 12 |Isoform 2: Secreted. |Isoform 3: Secreted(Potential). |Isoform 4: Secreted (Potential). ADML_HUMAN ADM ADMLungCancers, Secreted. UniProt, Literature, Benign- Detection, Nodules,Prediction Symptoms AGR2_HUMAN Anterior AGR2 EPI LungCancers Secreted.UniProt, Prediction gradient Endoplasmic protein 2 reticulum homolog (Bysimilarity). AIFM1_HUMAN Apoptosis- AIFM1 EPI, ENDO LungCancersMitochondrion Detection, inducing inter- Prediction factor 1, membranemitochondrial space. Nucleus. Note = Translocated to the nucleus uponinduction of apoptosis. ALDOA_HUMAN Fructose- ALDOA Secreted,LungCancers, Literature, bisphosphate EPI Symptoms Detection aldolase AAMPN_HUMAN Aminopeptidase N ANPEP EPI, ENDO LungCancers, Cell membrane;UniProt, Detection Benign- Single- Nodules, pass Symptoms type IImembrane protein. Cytoplasm, cytosol (Potential). Note = A soluble formhas also been detected. ANGP1_HUMAN Angiopoietin-1 ANGPT1 LungCancers,Secreted. UniProt, Literature, Benign- Prediction Nodules ANGP2_HUMANAngiopoietin-2 ANGPT2 LungCancers, Secreted. UniProt, Literature,Benign- Prediction Nodules APOA1_HUMAN Apolipo- APOA1 LungCancers,Secreted. UniProt, Literature, protein A-I Benign- Detection, Nodules,Prediction Symptoms APOE_HUMAN Apolipo- APOE EPI, ENDO LungCancers,Secreted. UniProt, Literature, protein E Benign- Detection, Nodules,Prediction Symptoms ASM3B_HUMAN Acid SMPDL3B EPI, ENDO Secreted (ByUniProt, Prediction sphingo- similarity). myelinase- likephosphodiesterase 3b AT2A2_HUMAN Sarcoplas- ATP2A2 EPI, ENDOLungCancers, Endoplasmic Detection plasmic/ Benign- reticulumendoplasmic Nodules membrane; reticulum Multi- calcium pass ATPase 2membrane protein. Sarcoplasmic reticulum membrane; Multi-pass membraneprotein. ATS1_HUMAN A disintegrin ADAMTS1 LungCancers, Secreted,UniProt, Literature, and Benign- extracellular Prediction metallo-Nodules, space, extra- proteinase Symptoms cellular matrix with (Bysimilarity). thrombospondin motifs 1 ATS12_HUMAN A disintegrin ADAMTS12LungCancers Secreted, UniProt, Detection, and extracellular Predictionmetallo- space, extra- proteinase cellular matrix with (By similarity).thrombospondin motifs 12 ATS19_HUMAN A disintegrin ADAMTS19 LungCancersSecreted, UniProt, Prediction and extracellular metallo- space, extra-proteinase cellular matrix with (By similarity). thrombospondin motifs19 BAGE1_HUMAN B melanoma BAGE LungCancers Secreted UniProt, Predictionantigen 1 (Potential). BAGE2_HUMAN B melanoma BAGE2 LungCancers SecretedUniProt, Prediction antigen 2 (Potential). BAGE3_HUMAN B melanoma BAGE3LungCancers Secreted UniProt, Prediction antigen 3 (Potential).BAGE4_HUMAN B melanoma BAGE4 LungCancers Secreted UniProt, Predictionantigen 4 (Potential). BAGE5_HUMAN B melanoma BAGE5 LungCancers SecretedUniProt, Prediction antigen 5 (Potential). BASP1_HUMAN Brain acid BASP1Secreted, Cell membrane; Detection soluble EPI Lipid- protein 1 anchor.Cell projection, growth cone. Note = Associated with the membranes ofgrowth cones that form the tips of elongating axons. BAX_HUMAN ApoptosisBAX EPI LungCancers, Isoform Alpha: UniProt, Literature, regulatorBenign- Mitochondrion Prediction BAX Nodules membrane; Single-passmembrane protein. Cytoplasm. Note = Colocalizes with 14-3-3 proteins inthe cytoplasm. Under stress conditions, redistributes to themitochondrion membrane through the release from JNK- phosphorylated14-3-3 proteins. |Isoform Beta: Cytoplasm. |Isoform Gamma: Cytoplasm.|Isoform Delta: Cytoplasm (Potential). BDNF_HUMAN Brain- BDNF Benign-Secreted. UniProt, Literature, derived Nodules, Prediction neurotrophicSymptoms factor BGH3_HUMAN Transforming TGFBI LungCancers, Secreted,UniProt, Detection growth Benign- extracellular factor- Nodules space,extra- beta- cellular matrix. induced Note = May protein igh3 beassociated both with microfibrils and with the cell surface. BMP2_HUMANBone BMP2 LungCancers, Secreted. UniProt, Literature morphogeneticBenign- protein 2 Nodules, Symptoms BST1_HUMAN ADP- BST1 EPI SymptomsCell membrane; Detection, ribosyl Lipid- Prediction cyclase 2 anchor,GPI-anchor. C163A_HUMAN Scavenger CD163 EPI Symptoms Soluble UniProt,Detection receptor CD163: Secreted. cysteine- |Cell rich type 1membrane; protein Single-pass M130 type I membrane protein. Note =Isoform 1 and isoform 2 show a lower surface expression when expressedin cells. C4BPA_HUMAN C4b- C4BPA LungCancers, Secreted. UniProt,Detection, binding Symptoms Prediction protein alpha chain CAH9_HUMANCarbonic CA9 LungCancers, Nucleus. UniProt anhydrase 9 Benign- Nucleus,Nodules, nucleolus. Symptoms Cell membrane; Single- pass type I membraneprotein. Cell projection, microvillus membrane; Single-pass type Imembrane protein. Note = Found on the surface micro- villi and in thenucleus, particularly in nucleolus. CALR_HUMAN Calreticulin CALR EPISymptoms Endoplasmic UniProt, Literature, reticulum Detection, lumen.Prediction Cytoplasm, cytosol. Secreted, extracellular space, extra-cellular matrix. Cell surface. Note = Also found in cell surface (Tcells), cytosol and extracellular matrix. Associated with the lyticgranules in the cytolytic T- lymphocytes. CALU_HUMAN Calumenin CALU EPISymptoms Endoplasmic UniProt, Detection, reticulum Prediction lumen.Secreted. Melanosome. Sarcoplasmic reticulum lumen (By similarity). Note= Identified by mass spectrometry in melanosome fractions from stage Ito stage IV. CALX_HUMAN Calnexin CANX Secreted, Benign- EndoplasmicUniProt, Literature, EPI, ENDO Nodules reticulum Detection membrane;Single- pass type I membrane protein. Melanosome. Note = Identified bymass spectrometry in melanosome fractions from stage I to stage IV.CAP7_HUMAN Azurocidin AZU1 EPI Symptoms Cytoplasmic Prediction granule.Note = Cytoplasmic granules of neutrophils. CATB_HUMAN Cathepsin B CTSBSecreted LungCancers Lysosome. Literature, Melanosome. Detection, Note =Identified Prediction by mass spectrometry in melanosome fractions fromstage I to stage IV. CATG_HUMAN Cathepsin G CTSG Secreted, Benign- Cellsurface. Detection, ENDO Nodules Prediction CBPB2_HUMAN Carboxy- CPB2LungCancers, Secreted. UniProt, Detection, peptidase Benign- PredictionB2 Nodules, Symptoms CCL22_HUMAN C-C motif CCL22 LungCancers, Secreted.UniProt, Prediction chemokine Benign- 22 Nodules CD14_HUMAN MonocyteCD14 EPI LungCancers, Cell membrane; Literature, differentiation Benign-Lipid- Detection, antigen Nodules, anchor, Prediction CD14 SymptomsGPI-anchor. CD24_HUMAN Signal CD24 LungCancers, Cell membrane;Literature transducer Benign- Lipid- CD24 Nodules anchor, GPI-anchor.CD2A2_HUMAN Cyclin- CDKN2A LungCancers, Cytoplasm. Literature, dependentBenign- Nucleus. Prediction kinase Nodules |Nucleus, inhibitor nucleolus2A, isoform 4 (By similarity). CD38_HUMAN ADP- CD38 EPI, ENDO SymptomsMembrane; UniProt, Literature ribosyl Single-pass cyclase 1 type IImembrane protein. CD40L_HUMAN CD40 CD40LG LungCancers, Cell membrane;UniProt, Literature ligand Benign- Single- Nodules, pass Symptoms typeII membrane protein. |CD40 ligand, soluble form: Secreted. CD44_HUMANCD44 CD44 EPI LungCancers, Membrane; UniProt, Literature, antigenBenign- Single-pass Detection, Nodules, type I membrane PredictionSymptoms protein. CD59_HUMAN CD59 CD59 LungCancers, Cell membrane;UniProt, Literature, glycoprotein Benign- Lipid- Detection, Nodules,anchor, Prediction Symptoms GPI-anchor. Secreted. Note = Soluble formfound in a number of tissues. CD97_HUMAN CD97 CD97 EPI, ENDO SymptomsCell membrane; UniProt antigen Multi- pass membrane protein. |CD97antigen sub- unit alpha: Secreted, extracellular space. CDCP1_HUMAN CUBdomain- CDCP1 LungCancers Isoform 1: UniProt, Prediction containing Cellmembrane; protein 1 Single- pass membrane protein (Potential). Note =Shedding may also lead to a soluble peptide. |Isoform 3: Secreted.CDK4_HUMAN Cell division CDK4 LungCancers, Literature protein Symptomskinase 4 CEAM5_HUMAN Carcinoembryonic CEACAM5 EPI LungCancers, Cellmembrane; Literature, antigen Benign- Lipid- Prediction related Nodules,anchor, cell adhesion Symptoms GPI-anchor. molecule 5 CEAM8_HUMANCarcinoembryonic CEACAM8 EPI LungCancers Cell membrane; Detection,antigen- Lipid- Prediction related anchor, cell adhesion GPI-anchor.molecule 8 CERU_HUMAN Ceruloplasmin CP EPI LungCancers, Secreted.UniProt, Literature, Symptoms Detection, Prediction CH10_HUMAN 10 kDaHSPE1 ENDO LungCancers Mitochondrion Literature, heat shock matrix.Detection, protein, Prediction mitochondrial CH60_HUMAN 60 kDa HSPD1Secreted, LungCancers, Mitochondrion Literature, heat shock EPI, ENDOSymptoms matrix. Detection protein, mitochondrial CKAP4_HUMANCytoskeleton- CKAP4 EPI, ENDO LungCancers Endoplasmic UniProt associatedreticulum- protein 4 Golgi intermediate compartment membrane; Single-pass membrane protein (Potential). CL041_HUMAN Uncharacterized C12orf41ENDO Prediction protein C12orf41 CLCA1_HUMAN Calcium- CLCA1 LungCancers,Secreted, UniProt, Prediction activated Benign- extracellular chlorideNodules space. Cell channel membrane; regulator 1 Peripheral membraneprotein; Extracellular side. Note = Protein that remains attached to theplasma membrane appeared to be predominantly localized to microvilli.CLIC1_HUMAN Chloride CLIC1 EPI Nucleus. UniProt, Literature,intracellular Nucleus Detection channel membrane; protein 1 Single-passmembrane protein (Probable). Cytoplasm. Cell membrane; Single- passmembrane protein (Probable). Note = Mostly in the nucleus including inthe nuclear membrane. Small amount in the cytoplasm and the plasmamembrane. Exists both as soluble cytoplasmic protein and as membraneprotein with probably a single transmembrane domain. CLUS_HUMANClusterin CLU EPI, ENDO LungCancers, Secreted. UniProt, Literature,Benign- Detection, Nodules, Prediction Symptoms CMGA_HUMANChromogranin-A CHGA LungCancers, Secreted. UniProt, Literature, Benign-Note = Neuro Detection, Nodules endocrine Prediction and endocrinesecretory granules. CNTN1_HUMAN Contactin-1 CNTN1 LungCancers Isoform 1:Detection, Cell membrane; Prediction Lipid- anchor, GPI-anchor;Extracellular side.|Isoform 2: Cell membrane; Lipid- anchor, GPI-anchor; Extracellular side. CO4A1_HUMAN Collagen COL4A1 LungCancersSecreted, UniProt, Detection, alpha- extracellular Prediction 1(IV)space, extra- chain cellular matrix, basement membrane. CO5A2_HUMANCollagen COL5A2 LungCancers Secreted, UniProt, Detection, alpha-extracellular Prediction 2(V) chain space, extra- cellular matrix (Bysimilarity). CO6A3_HUMAN Collagen COL6A3 Secreted Symptoms Secreted,UniProt, Detection, alpha- extracellular Prediction 3(VI) space, extra-chain cellular matrix (By similarity). COCA1_HUMAN Collagen COL12A1 ENDOLungCancers, Secreted, UniProt, Prediction alpha- Symptoms extracellular1(XII) space, extra- chain cellular matrix (By similarity). COF1_HUMANCofilin-1 CFL1 Secreted, LungCancers, Nucleus Detection, EPI Benign-matrix. Cytoplasm, Prediction Nodules cytoskeleton. Note = Almostcompletely in nucleus in cells exposed to heat shock or 10% di- methylsulfoxide. COIA1_HUMAN Collagen COL18A1 LungCancers, Secreted, UniProt,Literature, alpha- Benign- extracellular Detection, 1(XVIII) Nodulesspace, extra- Prediction chain cellular matrix (By similarity).COX5A_HUMAN Cytochrome c COX5A Secreted, Mitochondrion Predictionoxidase ENDO inner subunit membrane. 5A, mitochondrial CRP_HUMANC-reactive CRP LungCancers, Secreted. UniProt, Literature, proteinBenign- Detection, Nodules, Prediction Symptoms CS051_HUMAN UPF0470C19orf51 ENDO Prediction protein C19orf51 CSF1_HUMAN Macrophage CSF1LungCancers, Cell membrane; UniProt, Literature, colony- Benign- Single-Detection stimulating Nodules pass factor 1 membrane protein (Bysimilarity). |Processed macrophage colony- stimulating factor 1:Secreted, extracellular space (By similarity). CSF2_HUMAN Granulocyte-CSF2 LungCancers, Secreted. UniProt, Literature, macrophage Benign-Prediction colony- Nodules stimulating factor CT085_HUMANUncharacterized C20orf85 LungCancers, Prediction protein Benign-C20orf85 Nodules CTGF_HUMAN Connective CTGF LungCancers, Secreted,UniProt, Literature, tissue Benign- extracellular Detection, growthNodules space, extra- Prediction factor cellular matrix (By similarity).Secreted (By similarity). CYR61_HUMAN Protein CYR61 LungCancers,Secreted. UniProt, Prediction CYR61 Benign- Nodules CYTA_HUMANCystatin-A CSTA LungCancers Cytoplasm. Literature, Detection CYTB_HUMANCystatin-B CSTB Secreted Cytoplasm. Literature, Nucleus. DetectionDDX17_HUMAN Probable DDX17 ENDO LungCancers, Nucleus. Detection, ATP-Benign- Prediction dependent Nodules RNA helicase DDX17 DEFB1_HUMANBeta- DEFB1 LungCancers, Secreted. UniProt, Prediction defensin 1Benign- Nodules DESP_HUMAN Desmoplakin DSP EPI, ENDO LungCancers Celljunction, Detection desmosome. Cytoplasm, cytoskeleton. Note = Innermost portion of the desmosomal plaque. DFB4A_HUMAN Beta- DEFB4ALungCancers, Secreted. UniProt defensin Benign- 4A Nodules DHI1L_HUMANHydroxysteroid HSD11B1L LungCancers Secreted UniProt, Prediction 11-(Potential). beta- dehydro- genase 1- like protein DMBT1_HUMAN Deletedin DMBT1 LungCancers, Secreted (By UniProt, Detection, malignant Benign-similarity). Prediction brain tumors 1 Nodules Note = Some proteinisoforms may be membrane- bound. Localized to the lumenal aspect ofcrypt cells in the small intestine. In the colon, seen in the lumenalaspect of surface epithelial cells. Formed in the ducts of von Ebnergland, and released into the fluid bathing the taste buds contained inthe taste papillae (By similarity). DMKN_HUMAN Dermokine DMKNLungCancers Secreted. UniProt, Detection, Prediction DPP4_HUMANDipeptidyl DPP4 EPI LungCancers, Dipeptidyl UniProt, Detection peptidase4 Benign- peptidase 4 Nodules, soluble Symptoms form: Secreted. |Cellmembrane; Single-pass type II membrane protein. DSG2_HUMAN Desmoglein-2DSG2 ENDO Symptoms Cell membrane; UniProt, Detection Single- pass type Imembrane protein. Cell junction, desmosome. DX39A_HUMAN ATP- DDX39A EPINucleus (By Prediction dependent similarity). RNA helicase DDX39ADX39B_HUMAN Spliceosome DDX39B EPI Nucleus. Prediction RNA helicaseNucleus DDX39B speckle. DYRK2_HUMAN Dual specificity DYRK2 ENDOLungCancers Cytoplasm. Literature tyrosine- Nucleus. phosphorylation-Note = Translocates regulated into kinase 2 the nucleus following DNAdamage. EDN2_HUMAN Endothelin-2 EDN2 LungCancers Secreted. UniProt,Prediction EF1A1_HUMAN Elongation EEF1A1 Secreted, LungCancers,Cytoplasm. Detection factor EPI Benign- 1-alpha 1 Nodules EF1D_HUMANElongation EEF1D Secreted, LungCancers Prediction factor EPI 1-deltaEF2_HUMAN Elongation EEF2 Secreted, Cytoplasm. Literature, factor 2 EPIDetection EGF_HUMAN Pro- EGF LungCancers, Membrane; UniProt, Literatureepidermal Benign- Single-pass growth Nodules, type I membrane factorSymptoms protein. EGFL6_HUMAN Epidermal EGFL6 LungCancers Secreted,UniProt, Detection, growth extracellular Prediction factor-like space,extra- protein 6 cellular matrix, basement membrane (By similarity).ENOA_HUMAN Alpha- ENO1 Secreted, LungCancers, Cytoplasm. Literature,enolase EPI, ENDO Benign- Cell membrane. Detection, Nodules, Cytoplasm,Prediction Symptoms myofibril, sarcomere, M- band. Note = Cantranslocate to the plasma membrane in either the homodimeric (alpha/alpha) or heterodimeric (alpha/ gamma) form. ENO1 is localized to the M-band.|Isoform MBP-1: Nucleus. ENOG_HUMAN Gamma- ENO2 EPI LungCancers,Cytoplasm Literature, enolase Symptoms (By similarity). Detection, CellPrediction membrane (By similarity). Note = Can translocate to theplasma membrane in either the homodimeric (alpha/ alpha) orheterodimeric (alpha/ gamma) form (By similarity). ENOX2_HUMAN Ecto-ENOX2 LungCancers Cell membrane. UniProt, Detection NOX di- Secreted,sulfide- extracellular thiol exchanger 2 space. Note = Extracellular andplasma membrane- associated. ENPL_HUMAN Endo- HSP90B1 Secreted,LungCancers, Endoplasmic Literature, plasmin EPI, ENDO Benign- reticulumDetection, Nodules, lumen. Prediction Symptoms Melanosome. Note =Identified by mass spectrometry in melanosome fractions from stage I tostage IV. EPHB6_HUMAN Ephrin EPHB6 LungCancers Membrane; UniProt,Literature type-B Single-pass receptor 6 type I membrane protein.|Isoform 3: Secreted (Probable). EPOR_HUMAN Erythro- EPOR LungCancers,Cell membrane; UniProt, Literature, poietin Benign- Single- Detectionreceptor Nodules, pass Symptoms type I membrane protein. |IsoformEPOR-S: Secreted. Note = Secreted and located to the cell surface.ERBB3_HUMAN Receptor ERBB3 LungCancers, Isoform 1: UniProt, Literature,tyrosine- Benign- Cell membrane; Prediction protein Nodules Single-kinase pass erbB-3 type I membrane protein. |Isoform 2: Secreted.EREG_HUMAN Proepiregulin EREG LungCancers Epiregulin: UniProt Secreted,extracellular space.|Proepiregulin: Cell membrane; Single- pass type Imembrane protein. ERO1A_HUMAN ERO1- ERO1L Secreted, Symptoms EndoplasmicPrediction like protein EPI, ENDO reticulum alpha membrane; Peripheralmembrane protein; Lumenal side. Note = The association with ERP44 isessential for its retention in the endoplasmic reticulum. ESM1_HUMANEndothelial ESM1 LungCancers, Secreted. UniProt, Prediction cell-Benign- specific Nodules molecule 1 EZRI_HUMAN Ezrin EZR SecretedLungCancers, Apical cell Literature, Benign- membrane; Detection,Nodules Peripheral Prediction membrane protein; Cytoplasmic side. Cellprojection. Cell projection, micro- villus membrane; Peripheral membraneprotein; Cytoplasmic side. Cell projection, ruffle membrane; Peripheralmembrane protein; Cytoplasmic side. Cytoplasm, cell cortex. Cytoplasm,cytoskeleton. Note = Localization to the apical membrane of parietalcells depends on the interaction with MPP5. Localizes to cell extensionsand peripheral processes of astrocytes (By similarity). Micro- villarperipheral membrane protein (cytoplasmic side). F10A1_HUMAN Hsc70- ST13EPI Cytoplasm Detection, interacting (By similarity). Prediction protein|Cytoplasm (Probable). FAM3C_HUMAN Protein FAM3C EPI, ENDO SecretedUniProt, Detection FAM3C (Potential). FAS_HUMAN Fatty acid FASN EPILungCancers, Cytoplasm. Literature, synthase Benign- Melanosome.Detection Nodules, Note = Identified Symptoms by mass spectrometry inmelanosome fractions from stage I to stage IV. FCGR1_HUMAN High affinityFCGR1A EPI LungCancers, Cell membrane; UniProt immuno- Benign- Single-globulin Nodules, pass gamma Fc Symptoms type I membrane receptor Iprotein. Note = Stabilized at the cell membrane through interaction withFCER1G. FGF10_HUMAN Fibroblast FGF10 LungCancers Secreted UniProt,Prediction growth (Potential). factor 10 FGF2_HUMAN Heparin- FGF2LungCancers, Literature binding Benign- growth Nodules, factor 2Symptoms FGF7_HUMAN Keratinocyte FGF7 LungCancers, Secreted. UniProt,Literature, growth Benign- Prediction factor Nodules FGF9_HUMAN Glia-FGF9 LungCancers Secreted. UniProt, Literature, activating Predictionfactor FGFR2_HUMAN Fibroblast FGFR2 LungCancers, Cell membrane; UniProt,Literature, growth Benign- Single- Prediction factor Nodules passreceptor 2 type I membrane protein. |Isoform 14: Secreted. |Isoform 19:Secreted. FGFR3_HUMAN Fibroblast FGFR3 LungCancers Membrane; UniProt,Literature, growth Single-pass Prediction factor type I membranereceptor 3 protein. FGL2_HUMAN Fibroleukin FGL2 Benign- Secreted.UniProt, Detection, Nodules, Prediction Symptoms FHIT_HUMAN Bis(5′- FHITLungCancers, Cytoplasm. Literature adenosyl)- Benign- triphosphataseNodules, Symptoms FIBA_HUMAN Fibrinogen FGA LungCancers, Secreted.UniProt, Literature, alpha Benign- Detection, chain Nodules, PredictionSymptoms FINC_HUMAN Fibronectin FN1 Secreted, LungCancers, Secreted,UniProt, Literature, EPI, ENDO Benign- extracellular Detection, Nodules,space, extra- Prediction Symptoms cellular matrix. FKB11_HUMAN Peptidyl-FKBP11 EPI, ENDO Membrane; UniProt, Prediction prolyl cis- Single-passtrans isomerase membrane FKBP11 protein (Potential). FOLH1_HUMANGlutamate FOLH1 ENDO LungCancers, Cell membrane; UniProt, Literaturecarboxy- Symptoms Single- peptidase 2 pass type II membrane protein.|Isoform PSMA′: Cytoplasm. FOLR1_HUMAN Folate FOLR1 LungCancers Cellmembrane; UniProt receptor Lipid- alpha anchor, GPI-anchor. Secreted(Probable). FOXA2_HUMAN Hepatocyte FOXA2 LungCancers Nucleus. Detection,nuclear Prediction factor 3-beta FP100_HUMAN Fanconi C17orf70 ENDOSymptoms Nucleus. Prediction anemia- associated protein of 100 kDaFRIH_HUMAN Ferritin FTH1 EPI LungCancers, Literature, heavy Benign-Detection, chain Nodules Prediction FRIL_HUMAN Ferritin FTL Secreted,Benign- Literature, light chain EPI, ENDO Nodules, Detection SymptomsG3P_HUMAN Glyceraldehyde- GAPDH Secreted, LungCancers, Cytoplasm.Detection 3- EPI, ENDO Benign- Cytoplasm, phosphate Nodules, perinucleardehydrogenase Symptoms region. Membrane. Note = Postnuclear andPerinuclear regions. G6PD_HUMAN Glucose- G6PD Secreted, LungCancers,Literature, 6- EPI Symptoms Detection phosphate 1- dehydrogenaseG6PI_HUMAN Glucose- GPI Secreted, Symptoms Cytoplasm. UniProt,Literature, 6- EPI Secreted. Detection phosphate isomerase GA2L1_HUMANGAS2- GAS2L1 ENDO Cytoplasm, Prediction like protein 1 cytoskeleton(Probable). GALT2_HUMAN Polypeptide GALNT2 EPI, ENDO Golgi apparatus,UniProt, Detection N- Golgi acetylgalactosaminyl- stack membrane;transferase 2 Single- pass type II membrane protein. Secreted. Note =Resides preferentially in the trans and medial parts of the Golgi stack.A secreted form also exists. GAS6_HUMAN Growth GAS6 LungCancersSecreted. UniProt, Detection, arrest- Prediction specific protein 6GDIR2_HUMAN Rho GDP- ARHGDIB EPI Cytoplasm. Detection dissociationinhibitor 2 GELS_HUMAN Gelsolin GSN LungCancers, Isoform 2: UniProt,Literature, Benign- Cytoplasm, Detection, Nodules cytoskeleton.Prediction |Isoform 1: Secreted. GGH_HUMAN Gamma- GGH LungCancersSecreted, UniProt, Detection, glutamyl extracellular Predictionhydrolase space. Lysosome. Melanosome. Note = While its intracellularlocation is primarily the lysosome, most of the enzyme activity issecreted. Identified by mass spectrometry in melanosome fractions fromstage I to stage IV. GPC3_HUMAN Glypican-3 GPC3 LungCancers, Cellmembrane; UniProt, Literature, Symptoms Lipid- Prediction anchor,GPI-anchor; Extracellular side (By similarity). |Secreted glypican-3:Secreted, extracellular space (By similarity). GRAN_HUMAN Grancalcin GCAEPI Cytoplasm. Prediction Cytoplasmic granule membrane; Peripheralmembrane protein; Cytoplasmic side. Note = Primarily cytosolic in theabsence of calcium or magnesium ions. Relocates to granules and othermembranes in response to elevated calcium and magnesium levels.GREB1_HUMAN Protein GREB1 ENDO Membrane; UniProt, Prediction GREB1Single-pass membrane protein (Potential). GREM1_HUMAN Gremlin-1 GREM1LungCancers, Secreted UniProt, Prediction Benign- (Probable). NodulesGRP_HUMAN Gastrin- GRP LungCancers, Secreted. UniProt, Predictionreleasing Symptoms peptide GRP78_HUMAN 78 kDa HSPA5 Secreted,LungCancers, Endoplasmic Detection, glucose- EPI, ENDO Benign- reticulumPrediction regulated Nodules lumen. protein Melanosome. Note =Identified by mass spectrometry in melanosome fractions from stage I tostage IV. GSLG1_HUMAN Golgi GLG1 EPI, ENDO Benign- Golgi apparatusUniProt apparatus Nodules membrane; protein 1 Single- pass type Imembrane protein. GSTP1_HUMAN Glutathione GSTP1 Secreted LungCancers,Literature, S- Benign- Detection, transferase P Nodules, PredictionSymptoms GTR1_HUMAN Solute SLC2A1 EPI, ENDO LungCancers, Cell membrane;Literature carrier Benign- Multi- family 2, Nodules, pass facilitatedSymptoms membrane glucose protein (By transporter similarity). member 1Melanosome. Note = Localizes primarily at the cell surface (Bysimilarity). Identified by mass spectrometry in melanosome fractionsfrom stage I to stage IV. GTR3_HUMAN Solute SLC2A3 EPI Membrane;Detection carrier Multi-pass family 2, membrane facilitated protein.glucose transporter member 3 H2A1_HUMAN Histone HIST1H2AG SecretedNucleus. Detection, H2A type 1 Prediction H2A1B_HUMAN Histone HIST1H2ABSecreted Nucleus. Detection, H2A type Prediction 1-B/E H2A1C_HUMANHistone HIST1H2AC Secreted Nucleus. Literature, H2A type Detection, 1-CPrediction H2A1D_HUMAN Histone HIST1H2AD Secreted Nucleus. Detection,H2A type Prediction 1-D HG2A_HUMAN HLA class CD74 LungCancers, Membrane;UniProt, Literature II histo- Benign- Single-pass compatibility Nodules,type II antigen Symptoms membrane gamma protein (Potential). chainHGF_HUMAN Hepatocyte HGF LungCancers, Literature, growth Benign-Prediction factor Nodules, Symptoms HMGA1_HUMAN High mobility HMGA1LungCancers, Nucleus. Literature group Benign- protein Nodules, HMG-Symptoms I/HMG-Y HPRT_HUMAN Hypoxanthine- HPRT1 EPI Cytoplasm.Detection, guanine Prediction phosphoribosyltransferase HPSE_HUMANHeparanase HPSE LungCancers, Lysosome UniProt, Prediction Benign-membrane; Nodules, Peripheral Symptoms membrane protein. Secreted. Note= Secreted, internalised and transferred to late endosomes/ lysosomes asa proheparanase. In lysosomes, it is processed into the active form, theheparanase. The uptake or internalisation of proheparanase is mediatedby HSPGs. Heparin appears to be a competitor and retain proheparanase inthe extracellular medium. HPT_HUMAN Haptoglobin HP LungCancers,Secreted. UniProt, Literature, Benign- Detection, Nodules, PredictionSymptoms HS90A_HUMAN Heat HSP90AA1 Secreted, LungCancers, Cytoplasm.Literature, shock EPI Symptoms Melanosome. Detection protein Note =Identified HSP 90- by mass alpha spectrometry in melanosome fractionsfrom stage I to stage IV. HS90B_HUMAN Heat HSP90AB1 Secreted,LungCancers Cytoplasm. Literature, shock EPI Melanosome. Detectionprotein Note = Identified HSP 90- by mass beta spectrometry inmelanosome fractions from stage I to stage IV. HSPB1_HUMAN Heat HSPB1Secreted, LungCancers, Cytoplasm. Literature, shock EPI Benign- Nucleus.Detection, protein Nodules Cytoplasm, Prediction beta-1 cytoskeleton,spindle. Note = Cytoplasmic in interphase cells. Colo- calizes withmitotic spindles in mitotic cells. Translocates to the nucleus duringheat shock. HTRA1_HUMAN Serine HTRA1 LungCancers Secreted. UniProt,Prediction protease HTRA1 HXK1_HUMAN Hexokinase-1 HK1 ENDO SymptomsMitochondrion Literature, outer Detection membrane. Note = Itshydrophobic N-terminal sequence may be in- volved in membrane binding.HYAL2_HUMAN Hyaluronidase-2 HYAL2 LungCancers Cell membrane; PredictionLipid- anchor, GPI-anchor. HYOU1_HUMAN Hypoxia HYOU1 EPI, ENDO SymptomsEndoplasmic Detection up- reticulum regulated lumen. protein 1IBP2_HUMAN Insulin- IGFBP2 LungCancers Secreted. UniProt, Literature,like Detection, growth Prediction factor- binding protein 2 IBP3_HUMANInsulin- IGFBP3 LungCancers, Secreted. UniProt, Literature, like Benign-Detection, growth Nodules, Prediction factor- Symptoms binding protein 3ICAM1_HUMAN Intercellular ICAM1 LungCancers, Membrane; UniProt,Literature, adhesion Benign- Single-pass Detection molecule 1 Nodules,type I membrane Symptoms protein. ICAM3_HUMAN Intercellular ICAM3 EPI,ENDO LungCancers, Membrane; UniProt, Detection adhesion Benign-Single-pass molecule 3 Nodules, type I membrane Symptoms protein.IDHP_HUMAN Isocitrate IDH2 Secreted, Mitochondrion. Prediction dehydro-ENDO genase [NADP], mitochondrial IF4A1_HUMAN Eukaryotic EIF4A1Secreted, Detection, initiation EPI, ENDO Prediction factor 4A-IIGF1_HUMAN Insulin- IGF1 LungCancers, Secreted. UniProt, Literature,like Benign- |Secreted. Detection, growth Nodules, Prediction factor ISymptoms IKIP_HUMAN Inhibitor IKIP ENDO Symptoms Endoplasmic UniProt,Prediction of nuclear reticulum factor membrane; kappa-B Single- kinase-pass interacting membrane protein protein. Note = Isoform 4 deletion ofthe hydrophobic, or transmembrane region between AA 45-63 results inuniform distribution troughout the cell, suggesting that this region isresponsible for endoplasmic reticulum localization. IL18_HUMANInterleukin- IL18 LungCancers, Secreted. UniProt, Literature, 18 Benign-Prediction Nodules, Symptoms IL19_HUMAN Interleukin- IL19 LungCancersSecreted. UniProt, Detection, 19 Prediction IL22_HUMAN Interleukin- IL22LungCancers, Secreted. UniProt, Prediction 22 Benign- Nodules IL32_HUMANInterleukin- IL32 LungCancers, Secreted. UniProt, Prediction 32 Benign-Nodules IL7_HUMAN Interleukin-7 IL7 LungCancers, Secreted. UniProt,Literature, Benign- Prediction Nodules IL8_HUMAN Interleukin-8 IL8LungCancers, Secreted. UniProt, Literature Benign- Nodules, SymptomsILEU_HUMAN Leukocyte SERPINB1 Secreted, Cytoplasm Detection, elastaseEPI (By similarity). Prediction inhibitor ILK_HUMAN Integrin- ILKSecreted LungCancers, Cell junction, Literature, linked Benign- focalDetection protein Nodules, adhesion. kinase Symptoms Cell membrane;Peripheral membrane protein; Cytoplasmic side. INHBA_HUMAN Inhibin INHBALungCancers, Secreted. UniProt, Literature, beta A Benign- Predictionchain Nodules ISLR_HUMAN Immuno- ISLR LungCancers Secreted UniProt,Detection, globulin (Potential). Prediction super- family containingleucine- rich repeat protein ITA5_HUMAN Integrin ITGA5 EPI LungCancers,Membrane; UniProt, Literature, alpha-5 Benign- Single-pass DetectionNodules, type I membrane Symptoms protein. ITAM_HUMAN Integrin ITGAMEPI, ENDO LungCancers, Membrane; UniProt, Literature alpha-M Benign-Single-pass Nodules, type I membrane Symptoms protein. K0090_HUMANUncharacterized KIAA0090 EPI Symptoms Membrane; UniProt, Predictionprotein Single-pass KI- type I membrane AA0090 protein (Potential).K1C18_HUMAN Keratin, KRT18 Secreted LungCancers, Cytoplasm, Literature,type I Benign- perinuclear Detection, cytoskeletal Nodules region.Prediction 18 K1C19_HUMAN Keratin, KRT19 LungCancers, Literature, type IBenign- Detection, cytoskeletal Nodules Prediction 19 K2C8_HUMANKeratin, KRT8 EPI LungCancers Cytoplasm. Literature, type II Detectioncytoskeletal 8 KIT_HUMAN Mast/stem KIT LungCancers Membrane; UniProt,Literature, cell Single-pass Detection growth type I membrane factorprotein. receptor KITH_HUMAN Thymidine TK1 LungCancers Cytoplasm.Literature, kinase, Prediction cytosolic KLK11_HUMAN Kallikrein- KLK11LungCancers Secreted. UniProt, Literature, 11 Prediction KLK13_HUMANKallikrein- KLK13 LungCancers Secreted UniProt, Literature, 13(Probable). Detection, Prediction KLK14_HUMAN Kallikrein- KLK14LungCancers, Secreted, UniProt, Literature, 14 Symptoms extracellularPrediction space. KLK6_HUMAN Kallikrein-6 KLK6 LungCancers, Secreted.UniProt, Literature, Benign- Nucleus, Detection, Nodules, nucleolus.Prediction Symptoms Cytoplasm. Mitochondrion. Microsome. Note = Inbrain, detected in the nucleus of glial cells and in the nucleus andcytoplasm of neurons. Detected in the mitochondrial and microsomalfractions of HEK-293 cells and released into the cytoplasm followingcell stress. KNG1_HUMAN Kininogen-1 KNG1 LungCancers, Secreted, UniProt,Detection, Benign- extracellular Prediction Nodules, space. SymptomsKPYM_HUMAN Pyruvate PKM2 Secreted, LungCancers, Cytoplasm. Literature,kinase EPI Symptoms Nucleus. Detection isozymes Note = TranslocatesM1/M2 to the nucleus in response to different apoptotic stimuli. Nucleartrans- location is sufficient to induce cell death that is caspaseindependent, isoform- specific and independent of its enzymaticactivity. KRT35_HUMAN Keratin, KRT35 ENDO Detection, type I Predictioncuticular Ha5 LAMB2_HUMAN Laminin LAMB2 ENDO LungCancers, Secreted,UniProt, Detection, subunit Symptoms extracellular Prediction beta-2space, extra- cellular matrix, basement membrane. Note = S- laminin isconcentrated in the synaptic cleft of the neuro- muscular junction.LDHA_HUMAN L-lactate LDHA Secreted, LungCancers Cytoplasm. Literature,dehydro- EPI, ENDO Detection, genase A Prediction chain LDHB_HUMANL-lactate LDHB EPI LungCancers Cytoplasm. Detection, dehydro- Predictiongenase B chain LEG1_HUMAN Galectin-1 LGALS1 Secreted LungCancersSecreted, UniProt, Detection extracellular space, extra- cellularmatrix. LEG3_HUMAN Galectin-3 LGALS3 LungCancers, Nucleus. Literature,Benign- Note = Cytoplasmic Detection, Nodules in Prediction adenomas andcarcinomas. May be secreted by a non- classical secretory pathway andassociate with the cell surface. LEG9_HUMAN Galectin-9 LGALS9 ENDOSymptoms Cytoplasm UniProt (By similarity). Secreted (By similarity).Note = May also be secreted by a non- classical secretory pathway (Bysimilarity). LG3BP_HUMAN Galectin- LGALS3BP Secreted LungCancers,Secreted. UniProt, Literature, 3-binding Benign- Secreted, Detection,protein Nodules, extracellular Prediction Symptoms space, extra-cellular matrix. LPLC3_HUMAN Long palate, C20orf185 LungCancers Secreted(By UniProt, Prediction lung similarity). and nasal Cytoplasm.epithelium Note = According carcinoma- to Pub- associated Pub- protein 3Med: 12837268 it is cytoplasmic. LPLC4_HUMAN Long palate, C20orf186LungCancers Secreted (By UniProt, Prediction lung similarity). and nasalCytoplasm. epithelium carcinoma- associated protein 4 LPPRC_HUMANLeucine- LRPPRC Secreted, LungCancers, Mitochondrion. Prediction richPPR ENDO Symptoms Nucleus, motif- nucleoplasm. containing Nucleusprotein, inner membrane. mitochondrial Nucleus outer membrane. Note =Seems to be pre- dominantly mitochondrial. LRP1_HUMAN Prolow- LRP1 EPILungCancers, Low-density UniProt, Detection density Symptoms lipoproteinlipoprotein receptor- receptor- related protein related 1 85 kDa protein1 subunit: Cell membrane; Single- pass type I membrane protein.Membrane, coated pit.|Low- density lipo- protein receptor- relatedprotein 1 515 kDa subunit: Cell membrane; Peripheral membrane protein;Extracellular side. Membrane, coated pit.|Low- density lipo- proteinreceptor- related protein 1 intra- cellular domain: Cytoplasm. Nucleus.Note = After cleavage, the intracellular domain (LRPICD) is detectedboth in the cytoplasm and in the nucleus. LUM_HUMAN Lumican LUMSecreted, LungCancers, Secreted, UniProt, Detection, EPI Benign-extracellular Prediction Nodules, space, extra- Symptoms cellular matrix(By similarity). LY6K_HUMAN Lymphocyte LY6K LungCancers, Secreted.UniProt, Prediction antigen Symptoms Cytoplasm. 6K Cell membrane; Lipid-anchor, GPI-anchor (Potential). LYAM2_HUMAN E-selectin SELE LungCancers,Membrane; UniProt, Literature, Benign- Single-pass Detection Nodules,type I membrane Symptoms protein. LYAM3_HUMAN P-selectin SELPLungCancers, Membrane; UniProt, Literature, Benign- Single-passDetection Nodules, type I membrane Symptoms protein. LYOX_HUMAN Protein-LOX LungCancers, Secreted, UniProt, Detection, lysine 6- Benign-extracellular Prediction oxidase Nodules space. LYPD3_HUMAN Ly6/PLAURLYPD3 LungCancers Cell membrane; Detection, domain- Lipid- Predictioncontaining anchor, protein 3 GPI-anchor. MAGA4_HUMAN Melanoma- MAGEA4LungCancers Literature, associated Prediction antigen 4 MASP1_HUMANMannan- MASP1 LungCancers, Secreted. UniProt, Detection, bindingSymptoms Prediction lectin serine protease 1 MDHC_HUMAN Malate MDH1Secreted Cytoplasm. Literature, dehydro- Detection, genase, Predictioncytoplasmic MDHM_HUMAN Malate MDH2 ENDO LungCancers MitochondrionDetection, dehydro- matrix. Prediction genase, mitochondrial MIF_HUMANMacrophage MIF Secreted LungCancers, Secreted. UniProt, Literature,migration Benign- Cytoplasm. Prediction inhibitory Nodules, Note = Doesfactor Symptoms not have a cleavable signal sequence and is secreted viaa specialized, non-classical pathway. Secreted by macrophages uponstimulation by bacterial lipopolysaccharide (LPS), or by M. tuberculosisantigens. MLH1_HUMAN DNA MLH1 ENDO LungCancers, Nucleus. Literaturemismatch Benign- repair Nodules, protein Symptoms Mlh1 MMP1_HUMANInterstitial MMP1 LungCancers, Secreted, UniProt, Literature,collagenase Benign- extracellular Prediction Nodules, space, extra-Symptoms cellular matrix (Probable). MMP11_HUMAN Stromelysin-3 MMP11LungCancers, Secreted, UniProt, Literature, Symptoms extracellularPrediction space, extra- cellular matrix (Probable). MMP12_HUMANMacrophage MMP12 LungCancers, Secreted, UniProt, Literature,metalloelastase Benign- extracellular Prediction Nodules, space, extra-Symptoms cellular matrix (Probable). MMP14_HUMAN Matrix MMP14 ENDOLungCancers, Membrane; UniProt, Literature, metallo- Benign- Single-passDetection proteinase- Nodules, type I membrane 14 Symptoms protein(Potential). Melanosome. Note = Identified by mass spectrometry inmelanosome fractions from stage I to stage IV. MMP2_HUMAN 72 kDa MMP2LungCancers, Secreted, UniProt, Literature, type IV Benign-extracellular Detection, collagenase Nodules, space, extra- PredictionSymptoms cellular matrix (Probable). MMP26_HUMAN Matrix MMP26LungCancers Secreted, UniProt, Prediction metallo- extracellularproteinase- space, extra- 26 cellular matrix. MMP7_HUMAN Matrilysin MMP7LungCancers, Secreted, UniProt, Literature, Benign- extracellularPrediction Nodules, space, extra- Symptoms cellular matrix (Probable).MMP9_HUMAN Matrix MMP9 LungCancers, Secreted, UniProt, Literature,metallo- Benign- extracellular Detection, proteinase-9 Nodules, space,extra- Prediction Symptoms cellular matrix (Probable). MOGS_HUMANMannosyl- MOGS ENDO Endoplasmic UniProt, Prediction oligosaccharidereticulum glucosidase membrane; Single- pass type II membrane protein.MPRI_HUMAN Cation- IGF2R EPI, ENDO LungCancers, Lysosome UniProt,Literature, independent Symptoms membrane; Detection mannose-Single-pass 6- type I membrane phosphate protein. receptor MRP3_HUMANCanalicular ABCC3 EPI LungCancers Membrane; Literature, multi-Multi-pass Detection specific membrane organic protein. aniontransporter 2 MUC1_HUMAN Mucin-1 MUC1 EPI LungCancers, Apical cellUniProt, Literature, Benign- membrane; Prediction Nodules, Single-passSymptoms type I membrane protein. Note = Exclusively located in theapical domain of the plasma membrane of highly polarized epithelialcells. After endocytosis, internalized and recycled to the cellmembrane. Located to microvilli and to the tips of long filopodialprotusitusisions. |Isoform 5: Secreted. |Isoform 7: Secreted. |Isoform9: Secreted. |Mucin-1 subunit beta: Cell membrane. Cytoplasm. Nucleus.Note = On EGF and PDGFRB stimulation, transported to the nucleus throughinteraction with CTNNB1, a process which is stimulated byphosphorylation. On HRG stimulation, colocalizes with JUP/gamma- cateninat the nucleus. MUC16_HUMAN Mucin-16 MUC16 LungCancers Cell membrane;UniProt, Detection Single- pass type I membrane protein. Secreted,extracellular space. Note = May be liberated into the extracellularspace following the phosphorylation of the intracellular C-terminuswhich induces the proteolytic cleavage and liberation of theextracellular domain. MUC4_HUMAN Mucin-4 MUC4 LungCancers, Membrane;UniProt Benign- Single-pass Nodules membrane protein (Potential).Secreted. Note = Isoforms lacking the Cys-rich region, EGF-like domainsand transmembrane region are secreted. Secretion occurs by splicing orproteolytic processcessing. |Mucin-4 beta chain: Cell membrane; Single-pass membrane protein. |Mucin- 4 alpha chain: creted. |Isoform 3: Cellmembrane; Single-pass membrane protein. |Isoform 15: Secreted.MUC5B_HUMAN Mucin-5B MUC5B LungCancers, Secreted. UniProt, Detection,Benign- Prediction Nodules MUCL1_HUMAN Mucin- MUCL1 LungCancers SecretedUniProt, Prediction like protein 1 (Probable). Membrane (Probable).NAMPT_HUMAN Nicotinamide NAMPT EPI LungCancers, Cytoplasm Literature,phosphoribosyltransferase Benign- (By similarity). Detection Nodules,Symptoms NAPSA_HUMAN Napsin-A NAPSA Secreted LungCancers PredictionNCF4_HUMAN Neutrophil NCF4 ENDO Cytoplasm. Prediction cytosol factor 4NDKA_HUMAN Nucleoside NME1 Secreted LungCancers, Cytoplasm. Literature,di- Benign- Nucleus. Detection phosphate Nodules, Note = Cell- kinase ASymptoms cycle dependent nuclear localization which can be induced byinteraction with Epstein- barr viral proteins or by degradation of theSET complex by GzmA. NDKB_HUMAN Nucleoside NME2 Secreted, Benign-Cytoplasm. Literature, di- EPI Nodules Nucleus. Detection phosphate Note= Isoform kinase B 2 is mainly cytoplasmic and isoform 1 and isoform 2are excluded from the nucleolus. NDUS1_HUMAN NADH- NDUFS1 Secreted,Symptoms Mitochondrion Prediction ubiquinone ENDO inner oxidoreductasemembrane. 75 kDa subunit, mitochondrial NEBL_HUMAN Nebulette NEBL ENDOPrediction NEK4_HUMAN Serine/ NEK4 ENDO LungCancers Nucleus Predictionthreonine (Probable). protein kinase Nek4 NET1_HUMAN Netrin-1 NTN1LungCancers, Secreted, UniProt, Literature, Benign- extracellularPrediction Nodules space, extra- cellular matrix (By similarity).NEU2_HUMAN Vasopressin AVP LungCancers, Secreted. UniProt, Predictionneurophysin Symptoms 2- copeptin NGAL_HUMAN Neutrophil LCN2 EPILungCancers, Secreted. UniProt, Detection, gelatinase- Benign-Prediction associated Nodules, lipocalin Symptoms NGLY1_HUMAN Peptide-NGLY1 ENDO Cytoplasm. Detection, N(4)-(N- Prediction acetyl- beta-glucosamiminyl)asparagine amidase NHRF1_HUMAN Na(+)/H(+) SLC9A3R1 EPIBenign- Endomembrane Detection exchange Nodules system; regulatoryPeripheral cofactor membrane NHE-RF1 protein. Cell projection,filopodium. Cell projection, ruffle. Cell projection, microvillus. Note= Colocalizes with actin in microvilli- rich apical regions of thesyncytio- trophoblast. Found in microvilli, ruffling membrane andfilopodia of HeLa cells. Present in lipid rafts of T- cells. NIBAN_HUMANProtein FAM129A EPI Cytoplasm. Literature, Niban Detection NMU_HUMANNeuromedin-U NMU LungCancers Secreted. UniProt, Prediction NRP1_HUMANNeuropilin-1 NRP1 LungCancers, Cell membrane; UniProt, Literature,Benign- Single- Detection, Nodules, pass Prediction Symptoms type Imembrane protein. |Isoform 2: Secreted. ODAM_HUMAN Odontogenic ODAMLungCancers Secreted (By UniProt, Prediction ameloblast- similarity).associated protein OSTP_HUMAN Osteopontin SPP1 LungCancers, Secreted.UniProt, Literature, Benign- Detection, Nodules, Prediction SymptomsOVOS2_HUMAN Ovostatin OVOS2 ENDO Secreted (By UniProt, Predictionhomolog 2 similarity). P5CS_HUMAN Delta-1- ALDH18A1 ENDO MitochondrionPrediction pyrroline- inner 5- membrane. carboxylate synthasePA2GX_HUMAN Group 10 PLA2G10 Symptoms Secreted. UniProt secretoryphospholipase A2 PAPP1_HUMAN Pappalysin-1 PAPPA LungCancers, Secreted.UniProt, Literature, Benign- Prediction Nodules, Symptoms PBIP1_HUMANPre-B-cell PBXIP1 EPI Cytoplasm, Prediction leukemia cytoskeleton.transcription Nucleus. factor- Note = Shuttles interacting betweenprotein 1 the nucleus and the cytosol. Mainly localized in thecytoplasm, associated with microtubules. Detected in small amounts inthe nucleus. PCBP1_HUMAN Poly(rC)- PCBP1 EPI, ENDO Nucleus. Detection,binding Cytoplasm. Prediction protein 1 Note = Loosely bound in thenucleus. May shuttle between the nucleus and the cytoplasm. PCBP2_HUMANPoly(rC)- PCBP2 EPI Nucleus. Detection, binding Cytoplasm. Predictionprotein 2 Note = Loosely bound in the nucleus. May shuttle between thenucleus and the cytoplasm. PCD15_HUMAN Protocadherin- PCDH15 ENDO Cellmembrane; UniProt, Detection 15 Single- pass type I membrane protein (Bysimilarity). |Isoform 3: Secreted. PCNA_HUMAN Proliferating PCNA EPILungCancers, Nucleus. Literature, cell Benign- Prediction nuclearNodules, antigen Symptoms PCYOX_HUMAN Prenylcysteine PCYOX1 SecretedLungCancers, Lysosome. Detection, oxidase 1 Symptoms PredictionPDGFA_HUMAN Platelet- PDGFA LungCancers Secreted. UniProt, Literature,derived Prediction growth factor subunit A PDGFB_HUMAN Platelet- PDGFBLungCancers, Secreted. UniProt, Literature, derived Benign- Detection,growth Nodules, Prediction factor Symptoms subunit B PDGFD_HUMANPlatelet- PDGFD LungCancers Secreted. UniProt, Prediction derived growthfactor D PDIA3_HUMAN Protein PDIA3 ENDO LungCancers EndoplasmicDetection, disulfide- reticulum Prediction isomerase lumen A3 (Bysimilarity). Melanosome. Note = Identified by mass spectrometry inmelanosome fractions from stage I to stage IV. PDIA4_HUMAN Protein PDIA4Secreted, Endoplasmic Detection, disulfide- EPI, ENDO reticulumPrediction isomerase lumen. A4 Melanosome. Note = Identified by massspectrometry in melanosome fractions from stage I to stage IV.PDIA6_HUMAN Protein PDIA6 Secreted, Endoplasmic Detection, disulfide-EPI, ENDO reticulum Prediction isomerase lumen A6 (By similarity).Melanosome. Note = Identified by mass spectrometry in melanosomefractions from stage I to stage IV. PECA1_HUMAN Platelet PECAM1LungCancers, Membrane; UniProt, Literature, endothelial Benign-Single-pass Detection cell Nodules, type I membrane adhesion Symptomsprotein. molecule PEDF_HUMAN Pigment SERPINF1 LungCancers, Secreted.UniProt, Literature, epithelium- Symptoms Melanosome. Detection, derivedNote = Enriched Prediction factor in stage I melanosomes. PERM_HUMANMyeloperoxidase MPO Secreted, LungCancers, Lysosome. Literature, EPI,ENDO Benign- Detection, Nodules, Prediction Symptoms PERP1_HUMAN PlasmaPACAP EPI, ENDO Secreted UniProt, Detection, cell- (Potential).Prediction induced Cytoplasm. resident Note = In endoplasmic (Pub-reticulum Med: 11350957) protein diffuse granular localization in thecytoplasm surrounding the nucleus. PGAM1_HUMAN Phospho- PGAM1 Secreted,LungCancers, Detection glycerate EPI Symptoms mutase 1 PLAC1_HUMANPlacenta- PLAC1 LungCancers Secreted UniProt, Prediction specific(Probable). protein 1 PLACL_HUMAN Placenta- PLAC1L LungCancers SecretedUniProt, Prediction specific 1- (Potential). like protein PLIN2_HUMANPerilipin-2 ADFP ENDO LungCancers Membrane; Prediction Peripheralmembrane protein. PLIN3_HUMAN Perilipin-3 M6PRBP1 EPI Cytoplasm.Detection, Endosome Prediction membrane; Peripheral membrane protein;Cytoplasmic side (Potential). Lipid droplet (Potential). Note = Membraneassociated on endosomes. Detected in the envelope and the core of lipidbodies and in lipid sails. PLOD1_HUMAN Procollagen- PLOD1 EPI, ENDORough endoplasmic Prediction lysine,2- reticulum oxoglutarate membrane;5- Peripheral dioxygenase 1 membrane protein; Lumenal side. PLOD2_HUMANProcollagen- PLOD2 ENDO Benign- Rough endoplasmic Prediction lysine,2-Nodules, reticulum oxoglutarate Symptoms membrane; 5- Peripheraldioxygenase 2 membrane protein; Lumenal side. PLSL_HUMAN Plastin-2 LCP1Secreted, LungCancers Cytoplasm, Detection, EPI cytoskeleton. PredictionCell junction. Cell projection. Cell projection, ruffle membrane;Peripheral membrane protein; Cytoplasmic side (By similarity). Note =Relocalizes to the immunological synapse between peripheral blood Tlymphocytes and anti- body- presenting cells in response tocostimulation through TCR/CD3 and CD2 or CD28. Associated with the actincytoskeleton at membrane ruffles (By similarity). Relocalizes toactin-rich cell projections upon serine phosphorylation. PLUNC_HUMANProtein PLUNC LungCancers, Secreted (By UniProt, Prediction PluncBenign- similarity). Nodules Note = Found in the nasal mucus (Bysimilarity). Apical side of airway epithelial cells. Detected in nasalmucus (By similarity). PLXB3_HUMAN Plexin-B3 PLXNB3 ENDO Membrane;UniProt, Detection, Single-pass Prediction type I membrane protein.PLXC1_HUMAN Plexin-C1 PLXNC1 EPI Membrane; UniProt, DetectionSingle-pass type I membrane protein (Potential). POSTN_HUMAN PeriostinPOSTN Secreted, LungCancers, Secreted, UniProt, Literature, ENDO Benign-extracellular Detection, Nodules, space, extra- Prediction Symptomscellular matrix. PPAL_HUMAN Lysosomal ACP2 EPI Symptoms LysosomeUniProt, Prediction acid membrane; phosphatase Single-pass membraneprotein; Lumenal side. Lysosome lumen. Note = The soluble form arises byproteolytic processing of the membrane- bound form. PPBT_HUMAN AlkalineALPL EPI LungCancers, Cell membrane; Literature, phosphatase, Benign-Lipid- Detection, tissue- Nodules, anchor, Prediction nonspecificSymptoms GPI-anchor. isozyme PPIB_HUMAN Peptidyl- PPIB Secreted,Endoplasmic Detection, prolyl cis- EPI, ENDO reticulum Prediction transisomerase B lumen. Melanosome. Note = Identified by mass spectrometry inmelanosome fractions from stage I to stage IV. PRDX1_HUMANPeroxiredoxin-1 PRDX1 EPI LungCancers Cytoplasm. Detection, Melanosome.Prediction Note = Identified by mass spectrometry in melanosomefractions from stage I to stage IV. PRDX4_HUMAN Peroxiredoxin-4 PRDX4Secreted, Cytoplasm. Literature, EPI, ENDO Detection, PredictionPROF1_HUMAN Profilin-1 PFN1 Secreted, LungCancers Cytoplasm, DetectionEPI cytoskeleton. PRP31_HUMAN U4/U6 PRPF31 ENDO Nucleus Prediction smallnuclear speckle. ribo- Nucleus, nucleo- Cajal body. protein Note =Predominantly Prp31 found in speckles and in Cajal bodies. PRS6A_HUMAN26S protease PSMC3 EPI Benign- Cytoplasm Detection regulatory Nodules(Potential). subunit Nucleus 6A (Potential). PSCA_HUMAN Prostate PSCALungCancers Cell membrane; Literature, stem cell Lipid- Predictionantigen anchor, GPI-anchor. PTGIS_HUMAN Prostacyclin PTGIS EPILungCancers, Endoplasmic UniProt, Detection, synthase Benign- reticulumPrediction Nodules membrane; Single- pass membrane protein. PTPA_HUMANSerine/ PPP2R4 ENDO Symptoms Detection, threonine- Prediction proteinphosphatase 2A activator PTPRC_HUMAN Receptor- PTPRC Secreted,LungCancers Membrane; UniProt, Detection, type tyrosine- EPI, ENDOSingle-pass Prediction protein type I membrane phosphatase C protein.PTPRJ_HUMAN Receptor- PTPRJ EPI LungCancers, Membrane; UniProt,Detection, type tyrosine- Symptoms Single-pass Prediction protein type Imembrane phosphatase protein. eta PVR_HUMAN Poliovirus PVR SymptomsIsoform Alpha: UniProt, Detection, receptor Cell Prediction membrane;Single-pass type I membrane protein. |Isoform Delta: Cell membrane;Single-pass type I membrane protein. |Isoform Beta: Secretcreted.|Isoform Gamma: Secreted. RAB32_HUMAN Ras- RAB32 EPI Mitochondrion.Prediction related protein Rab-32 RAGE_HUMAN Advanced AGER SecretedLungCancers, Isoform 1: UniProt, Literature glycosylation Benign- Cellmembrane; end Nodules Single- product- pass specific type I membranereceptor protein. |Isoform 2: Secreted. RAN_HUMAN GTP- RAN Secreted,LungCancers, Nucleus. Detection, binding EPI Benign- Cytoplasm.Prediction nuclear Nodules Melanosome. protein Note = Becomes Randispersed throughout the cytoplasm during mitosis. Identified by massspectrometry in melanosome fractions from stage I to stage IV.RAP2B_HUMAN Ras- RAP2B EPI Cell membrane; Prediction related Lipid-protein anchor; Rap-2b Cytoplasmicside (Potential). RAP2C_HUMAN Ras-RAP2C EPI Cell membrane; Prediction related Lipid- protein anchor;Rap-2c Cytoplasmic side (Potential). RCN3_HUMAN Reticulocalbin-3 RCN3EPI Symptoms Endoplasmic Prediction reticulum lumen (Potential).RL24_HUMAN 60S ribosomal RPL24 EPI Prediction protein L24 S10A1_HUMANProtein S100A1 Symptoms Cytoplasm. Literature, S100-A1 PredictionS10A6_HUMAN Protein S100A6 Secreted LungCancers Nucleus Literature,S100-A6 envelope. Detection, Cytoplasm. Prediction S10A7_HUMAN ProteinS100A7 LungCancers Cytoplasm. UniProt, Literature, S100-A7 Secreted.Detection, Note = Secreted Prediction by a non- classical secretorypathway. SAA_HUMAN Serum SAA1 Symptoms Secreted. UniProt, Literature,amyloid A Detection, protein Prediction SCF_HUMAN Kit ligand KITLGLungCancers, Isoform 1: UniProt, Literature Symptoms Cell membrane;Single- pass type I membrane protein (By similarity). Secreted (Bysimilarity). Note = Also exists as a secreted soluble form (isoform 1only) (By similarity). |Isoform 2: Cell membrane; Single-pass type Imembrane protein (By similarity). Cytoplasm, cytoskeleton (Bysimilarity). SDC1_HUMAN Syndecan-1 SDC1 LungCancers, Membrane; UniProt,Literature, Benign- Single-pass Detection Nodules, type I membraneSymptoms protein. SEM3G_HUMAN Semaphorin- SEMA3G LungCancers Secreted(By UniProt, Prediction 3G similarity). SEPR_HUMAN Seprase FAP ENDOSymptoms Cell membrane; UniProt, Literature, Single- Detection pass typeII membrane protein. Cell projection, lamellipodium membrane; Single-pass type II membrane protein. Cell projection, invadopodium membrane;Single- pass type II membrane protein. Note = Found in cell surfacelamel- lipodia, in- vadopodia and on shed vesicles. SERPH_HUMAN SerpinH1 SERPINH1 Secreted, LungCancers, Endoplasmic Detection, EPI, ENDOBenign- reticulum Prediction Nodules lumen. SFPA2_HUMAN Pulmonary SFTPA2Secreted LungCancers, Secreted, UniProt, Prediction surfactant- Benign-extracellular associated Nodules space, extra- protein A2 cellularmatrix. Secreted, extracellular space, surface film. SFTA1_HUMANPulmonary SFTPA1 Secreted LungCancers, Secreted, UniProt, Predictionsurfactant- Benign- extracellular associated Nodules, space, extra-protein A1 Symptoms cellular matrix. Secreted, extracellular space,surface film. SG3A2_HUMAN Secreto- SCGB3A2 LungCancers, Secreted.UniProt, Prediction globin Benign- family 3A Nodules member 2SGPL1_HUMAN Sphingosine- SGPL1 ENDO Endoplasmic UniProt, Prediction 1-reticulum phosphate membrane; lyase 1 Single- pass type III membraneprotein. SIAL_HUMAN Bone sialoprotein 2 IBSP LungCancers Secreted.UniProt, Literature, Prediction SLPI_HUMAN Antileukoproteinase SLPILungCancers, Secreted. UniProt, Literature, Benign- Detection, NodulesPrediction SMD3_HUMAN Small SNRPD3 Secreted Benign- Nucleus. Predictionnuclear Nodules ribonucleoprotein Sm D3 SMS_HUMAN Somato- SSTLungCancers Secreted. UniProt, Literature, statin Prediction SODM_HUMANSuperoxide SOD2 Secreted LungCancers, Mitochondrion Literature,dismutase Benign- matrix. Detection, [Mn], Nodules, Predictionmitochondrial Symptoms SORL_HUMAN Sortilin- SORL1 EPI LungCancers,Membrane; UniProt, Detection related Symptoms Single-pass receptor typeI membrane protein (Potential). SPB3_HUMAN Serpin B3 SERPINB3LungCancers, Cytoplasm. Literature, Benign- Note = Seems DetectionNodules to also be secreted in plasma by cancerous cells but at a lowlevel. SPB5_HUMAN Serpin B5 SERPINB5 LungCancers Secreted, UniProt,Detection extracellular space. SPON2_HUMAN Spondin-2 SPON2 LungCancers,Secreted, UniProt, Prediction Benign- extracellular Nodules space,extra- cellular matrix (By similarity). SPRC_HUMAN SPARC SPARCLungCancers, Secreted, UniProt, Literature, Benign- extracellularDetection, Nodules, space, extra- Prediction Symptoms cellular matrix,basement membrane. Note = In or around the basement membrane. SRC_HUMANProto- SRC ENDO LungCancers, Literature oncogene Benign- tyrosine-Nodules, protein Symptoms kinase Src SSRD_HUMAN Trans- SSR4 Secreted,Endoplasmic UniProt, Prediction locon- ENDO reticulum associatedmembrane; protein Single- subunit pass delta type I membrane protein.STAT1_HUMAN Signal STAT1 EPI LungCancers, Cytoplasm. Detectiontransducer Benign- Nucleus. and activator Nodules Note = Translocated ofinto transcription the nucleus 1- in response alpha/beta to IFN- gamma-induced tyrosine phosphorylation and dimerization. STAT3_HUMAN SignalSTAT3 ENDO LungCancers, Cytoplasm. Prediction transducer Benign-Nucleus. and activator Nodules, Note = Shuttles of Symptoms betweentranscription 3 the nucleus and the cytoplasm. Constitutive nuclearpresence is independent of tyrosine phosphorylation. STC1_HUMAN Stannio-STC1 LungCancers, Secreted. UniProt, Prediction calcin-1 SymptomsSTT3A_HUMAN Dolichyl- STT3A EPI Symptoms Endoplasmic Literaturediphosphooligo- reticulum saccharide-- membrane; protein Multi-glycosyl- pass transferase membrane subunit protein. STT3A TAGL_HUMANTransgelin TAGLN EPI LungCancers Cytoplasm Literature, (Probable).Prediction TARA_HUMAN TRIO and TRIOBP ENDO Nucleus. Detection, F-actin-Cytoplasm, Prediction binding cytoskeleton. protein Note = Localized toF- actin in a periodic pattern. TBA1B_HUMAN Tubulin TUBA1B EPILungCancers Detection alpha-1B chain TBB2A_HUMAN Tubulin TUBB2A EPILungCancers, Detection, beta-2A Benign- Prediction chain NodulesTBB3_HUMAN Tubulin TUBB3 EPI LungCancers, Detection beta-3 Benign- chainNodules TBB5_HUMAN Tubulin TUBB EPI LungCancers, Detection beta chainBenign- Nodules TCPA_HUMAN T- TCP1 EPI Cytoplasm. Prediction complexprotein 1 subunit alpha TCPD_HUMAN T- CCT4 EPI Cytoplasm. Detection,complex Melanosome. Prediction protein 1 Note = Identified subunit bymass delta spectrometry in melanosome fractions from stage I to stageIV. TCPQ_HUMAN T- CCT8 Secreted, Cytoplasm. Prediction complex EPIprotein 1 subunit theta TCPZ_HUMAN T- CCT6A Secreted, Cytoplasm.Detection complex EPI protein 1 subunit zeta TDRD3_HUMAN Tudor TDRD3ENDO Cytoplasm. Prediction domain- Nucleus. containing Note =Predominantly protein 3 cytoplasmic. Associated with activelytranslating polyribosomes and with mRNA stress granules. TENA_HUMANTenascin TNC ENDO LungCancers, Secreted, UniProt, Literature, Benign-extracellular Detection Nodules, space, extra- Symptoms cellular matrix.TENX_HUMAN Tenascin-X TNXB ENDO LungCancers, Secreted, UniProt,Detection, Symptoms extracellular Prediction space, extra- cellularmatrix. TERA_HUMAN Transitional VCP EPI LungCancers, Cytoplasm,Detection endo- Benign- cytosol. Nucleus. plasmic Nodules Note = Presentreticulum in the neuronal ATPase hyaline inclusion bodies specificallyfound in motor neurons from amyotrophic lateral sclerosis patients.Present in the Lewy bodies specifically found in neurons from Parkinsondisease patients. TETN_HUMAN Tetranectin CLEC3B LungCancers Secreted.UniProt, Literature, Detection, Prediction TF_HUMAN Tissue F3LungCancers, Membrane; UniProt, Literature factor Benign- Single-passNodules, type I membrane Symptoms protein. TFR1_HUMAN Transferrin TFRCSecreted, LungCancers, Cell membrane; UniProt, Literature, receptor EPI,ENDO Benign- Single- Detection protein 1 Nodules, pass Symptoms type IImembrane protein. Melanosome. Note = Identified by mass spectrometry inmelanosome fractions from stage I to stage IV.|Transferrin receptorprotein 1, serum form: Secreted. TGFA_HUMAN Protransforming TGFALungCancers, Transforming UniProt, Literature growth Benign- growthfactor Nodules factor alpha: alpha Secreted, extracellularspace.|Protransforming growth factor alpha: Cell membrane; Single- passtype I membrane protein. THAS_HUMAN Thromboxane-A TBXAS1 EPI, ENDOLungCancers, Membrane; Prediction synthase Benign- Multi-pass Nodules,membrane Symptoms protein. THY1_HUMAN Thy-1 THY1 EPI Symptoms Cellmembrane; Detection, membrane Lipid- Prediction glycoprotein anchor,GPI-anchor (By similarity). TIMP1_HUMAN Metallo- TIMP1 LungCancers,Secreted. UniProt, Literature, proteinase Benign- Detection, inhibitor 1Nodules, Prediction Symptoms TIMP3_HUMAN Metallo- TIMP3 LungCancers,Secreted, UniProt, Literature, proteinase Benign- extracellularPrediction inhibitor 3 Nodules space, extra- cellular matrix. TLL1_HUMANTolloid- TLL1 ENDO Secreted UniProt, Prediction like protein 1(Probable). TNF12_HUMAN Tumor TNFSF12 LungCancers, Cell membrane;UniProt necrosis Benign- Single- factor Nodules pass ligand type IIsuper- membrane family protein. member |Tumor 12 necrosis factor ligandsuperfamily member 12, secreted form: Secreted. TNR6_HUMAN Tumor FASLungCancers, Isoform 1: UniProt, Literature, necrosis Benign- Cellmembrane; Prediction factor Nodules, Single- receptor Symptoms passsuper- type I membrane family protein. member 6 |Isoform 2: Secreted.|Isoform 3: Secreted. |Isoform 4: Secreted. |Isoform 5: Secreted.|Isoform 6: Secreted. TPIS_HUMAN Tri- TPI1 Secreted, SymptomsLiterature, osephosphate EPI Detection, isomerase Prediction TRFL_HUMANLacto- LTF Secreted, LungCancers, Secreted. UniProt, Literature,transferrin EPI, ENDO Benign- Detection, Nodules, Prediction SymptomsTSP1_HUMAN Thrombospondin-1 THBS1 LungCancers, Literature, Benign-Detection, Nodules, Prediction Symptoms TTHY_HUMAN Transthyretin TTRLungCancers, Secreted. UniProt, Literature, Benign- Cytoplasm.Detection, Nodules Prediction TYPH_HUMAN Thymidine TYMP EPI LungCancers,Literature, phosphorylase Benign- Detection, Nodules, PredictionSymptoms UGGG1_HUMAN UDP- UGGT1 Secreted, Endoplasmic Detection,glucose:glyco ENDO reticulum Prediction protein lumen. glucosyl-Endoplasmic transferase 1 reticulum- Golgi intermediate compartment.UGGG2_HUMAN UDP- UGGT2 ENDO Endoplasmic Prediction glucose:glycoreticulum protein lumen. glucosyl- Endoplasmic transferase 2 reticulum-Golgi intermediate compartment. UGPA_HUMAN UTP-- UGP2 EPI SymptomsCytoplasm. Detection glucose-1- phosphate uridyl- dyl- yltransferaseUPAR_HUMAN Urokinase PLAUR LungCancers, Isoform 1: UniProt, Literature,plasminogen Benign- Cell membrane; Prediction activator Nodules, Lipid-surface Symptoms anchor, receptor GPI-anchor. |Isoform 2: Secreted(Probable). UTER_HUMAN Utero- SCGB1A1 LungCancers, Secreted. UniProt,Literature, globin Benign- Detection, Nodules, Prediction SymptomsVA0D1_HUMAN V-type ATP6V0D1 EPI Prediction proton ATPase subunit d1VAV3_HUMAN Guanine VAV3 ENDO Prediction nucleotide exchange factor VAV3VEGFA_HUMAN Vascular VEGFA LungCancers, Secreted. UniProt, Literature,endothelial Benign- Note = VEGF Prediction growth Nodules, 121 is acidicfactor A Symptoms and freely secreted. VEGF165 is more basic, hasheparin- binding properties and, although a signicant proportion remainscell- associated, most is freely secreted. VEGF189 is very basic, it iscell- associated after secretion and is bound avidly by heparin and theextracellular matrix, although it may be released as a soluble form byheparin, heparinase or plasmin. VEGFC_HUMAN Vascular VEGFC LungCancers,Secreted. UniProt, Literature, endothelial Benign- Prediction growthNodules factor C VEGFD_HUMAN Vascular FIGF LungCancers Secreted.UniProt, Literature, endothelial Prediction growth factor D VGFR1_HUMANVascular FLT1 LungCancers, Isoform UniProt, Literature, endothelialBenign- Flt1: Cell Detection, growth Nodules, membrane; Predictionfactor Symptoms Single-pass receptor 1 type I membrane protein. |IsoformsFlt1: Secreted. VTNC_HUMAN Vitronectin VTN ENDO Symptoms Secreted,UniProt, Literature, extracellular Detection, space. PredictionVWC2_HUMAN Brorin VWC2 LungCancers Secreted, UniProt, Predictionextracellular space, extra- cellular matrix, basement membrane (Bysimilarity). WNT3A_HUMAN Protein WNT3A LungCancers, Secreted, UniProt,Prediction Wnt-3a Symptoms extracellular space, extra- cellular matrix.WT1_HUMAN Wilms WT1 LungCancers, Nucleus. Literature, tumor Benign-Cytoplasm Prediction protein Nodules, (By similarity). Symptoms Note =Shuttles between nucleus and cytoplasm (By similarity). |Isoform 1:Nucleus speckle. |Isoform 4: Nucleus, nucleoplasm. ZA2G_HUMAN Zinc-AZGP1 LungCancers, Secreted. UniProt, Literature, alpha-2- SymptomsDetection, glycoprotein Prediction ZG16B_HUMAN Zymogen ZG16B LungCancersSecreted UniProt, Prediction granule (Potential). protein 16 homolog B

190 of these candidate protein biomarkers were shown to be measuredreproducibly in blood. A moderately powered multisite and unbiased studyof 242 blood samples from patients with PN was designed to determinewhether a statistically significant subpanel of proteins could beidentified to distinguish benign and malignant nodules of sizes under 2cm. The three sites contributing samples and clinical data to this studywere the University of Laval, University of Pennsylvania and New YorkUniversity.

In an embodiment of the invention, a panel of 15 proteins effectivelydistinguished between samples derived from patients with benign andmalignant nodules less than 2 cm diameter.

Bioinformatic and biostatistical analyses were used first to identifyindividual proteins with statistically significant differentialexpression, and then using these proteins to derive one or morecombinations of proteins or panels of proteins, which collectivelydemonstrated superior discriminatory performance compared to anyindividual protein. Bioinformatic and biostatistical methods are used toderive coefficients (C) for each individual protein in the panel thatreflects its relative expression level, i.e. increased or decreased, andits weight or importance with respect to the panel's net discriminatoryability, relative to the other proteins. The quantitative discriminatoryability of the panel can be expressed as a mathematical algorithm with aterm for each of its constituent proteins being the product of itscoefficient and the protein's plasma expression level (P) (as measuredby LC-SRM-MS), e.g. C×P, with an algorithm consisting of n proteinsdescribed as: C1×P1+C2×P2+C3×P3+ . . . +Cn×Pn. An algorithm thatdiscriminates between disease states with a predetermined level ofstatistical significance may be refers to a “disease classifier”. Inaddition to the classifier's constituent proteins with differentialexpression, it may also include proteins with minimal or no biologicvariation to enable assessment of variability, or the lack thereof,within or between clinical specimens; these proteins may be termedtypical native proteins and serve as internal controls for the otherclassifier proteins.

In certain embodiments, expression levels are measured by MS. MSanalyzes the mass spectrum produced by an ion after its production bythe vaporization of its parent protein and its separation from otherions based on its mass-to-charge ratio. The most common modes ofacquiring MS data are 1) full scan acquisition resulting in the typicaltotal ion current plot (TIC), 2) selected ion monitoring (SIM), and 3)selected reaction monitoring (SRM).

In certain embodiments of the methods provided herein, biomarker proteinexpression levels are measured by LC-SRM-MS. LC-SRM-MS is a highlyselective method of tandem mass spectrometry which has the potential toeffectively filter out all molecules and contaminants except the desiredanalyte(s). This is particularly beneficial if the analysis sample is acomplex mixture which may comprise several isobaric species within adefined analytical window. LC-SRM-MS methods may utilize a triplequadrupole mass spectrometer which, as is known in the art, includesthree quadrupole rod sets. A first stage of mass selection is performedin the first quadrupole rod set, and the selectively transmitted ionsare fragmented in the second quadrupole rod set. The resultanttransition (product) ions are conveyed to the third quadrupole rod set,which performs a second stage of mass selection. The product ionstransmitted through the third quadrupole rod set are measured by adetector, which generates a signal representative of the numbers ofselectively transmitted product ions. The RF and DC potentials appliedto the first and third quadrupoles are tuned to select (respectively)precursor and product ions that have m/z values lying within narrowspecified ranges. By specifying the appropriate transitions (m/z valuesof precursor and product ions), a peptide corresponding to a targetedprotein may be measured with high degrees of sensitivity andselectivity. Signal-to-noise ratio is superior to conventional tandemmass spectrometry (MS/MS) experiments, which select one mass window inthe first quadrupole and then measure all generated transitions in theion detector. LC-SRM-MS.

In certain embodiments, an SRM-MS assay for use in diagnosing ormonitoring lung cancer as disclosed herein may utilize one or morepeptides and/or peptide transitions derived from the proteins set forthin Table 6. In certain embodiments, the assay may utilize peptidesand/or peptide transitions from 100 or more, 150 or more, 200 or more,250 or more, 300 or more, 345 or more, or 371 or more biomarkerproteins. In certain embodiments, two or more peptides may be utilizedper biomarker proteins, and in certain of these embodiments three ormore of four or more peptides may be utilized. Similarly, in certainembodiments two or more transitions may be utilized per peptide, and incertain of these embodiments three or more; four or more; or five ormore transitions may be utilized per peptide. In one embodiment, anLC-SRM-MS assay for use in diagnosing lung cancer may measure theintensity of five transitions that correspond to selected peptidesassociated with each biomarker protein. The achievable limit ofquantification (LOQ) may be estimated for each peptide according to theobserved signal intensities during this analysis. For examples, for setsof target proteins associated with lung cancer see Table 12.

The expression level of a biomarker protein can be measured using anysuitable method known in the art, including but not limited to massspectrometry (MS), reverse transcriptase-polymerase chain reaction(RT-PCR), microarray, serial analysis of gene expression (SAGE), geneexpression analysis by massively parallel signature sequencing (MPSS),immunoassays (e.g., ELISA), immunohistochemistry (1HC), transcriptomics,and proteomics.

To evaluate the diagnostic performance of a particular set of peptidetransitions, a ROC curve is generated for each significant transition.

An “ROC curve” as used herein refers to a plot of the true positive rate(sensitivity) against the false positive rate (specificity) for a binaryclassifier system as its discrimination threshold is varied. A ROC curvecan be represented equivalently by plotting the fraction of truepositives out of the positives (TPR=true positive rate) versus thefraction of false positives out of the negatives (FPR=false positiverate). Each point on the ROC curve represents a sensitivity/specificitypair corresponding to a particular decision threshold. FIGS. 7 and 9provide a graphical representation of the functional relationshipbetween the distribution of biomarker or biomarker panel sensitivity andspecificity values in a cohort of diseased subjects and in a cohort ofnon-diseased subjects.

AUC represents the area under the ROC curve. The AUC is an overallindication of the diagnostic accuracy of 1) a biomarker or a panel ofbiomarkers and 2) a ROC curve. AUC is determined by the “trapezoidalrule.” For a given curve, the data points are connected by straight linesegments, perpendiculars are erected from the abscissa to each datapoint, and the sum of the areas of the triangles and trapezoids soconstructed is computed. In certain embodiments of the methods providedherein, a biomarker protein has an AUC in the range of about 0.75 to1.0. In certain of these embodiments, the AUC is in the range of about0.8 to 0.8, 0.9 to 0.95, or 0.95 to 1.0.

The methods provided herein are minimally invasive and pose little or norisk of adverse effects. As such, they may be used to diagnose, monitorand provide clinical management of subjects who do not exhibit anysymptoms of a lung condition and subjects classified as low risk fordeveloping a lung condition. For example, the methods disclosed hereinmay be used to diagnose lung cancer in a subject who does not presentwith a PN and/or has not presented with a PN in the past, but whononetheless deemed at risk of developing a PN and/or a lung condition.Similarly, the methods disclosed herein may be used as a strictlyprecautionary measure to diagnose healthy subjects who are classified aslow risk for developing a lung condition.

The present invention provides a method of determining the likelihoodthat a lung condition in a subject is cancer by measuring an abundanceof a panel of proteins in a sample obtained from the subject;calculating a probability of cancer score based on the proteinmeasurements and ruling out cancer for the subject if the score) islower than a pre-determined score, wherein when cancer is ruled out thesubject does not receive a treatment protocol. Treatment protocolsinclude for example pulmonary function test (PFT), pulmonary imaging, abiopsy, a surgery, a chemotherapy, a radiotherapy, or any combinationthereof. In some embodiments, the imaging is an x-ray, a chest computedtomography (CT) scan, or a positron emission tomography (PET) scan.

The present invention further provides a method of ruling in thelikelihood of cancer for a subject by measuring an abundance of panel ofproteins in a sample obtained from the subject, calculating aprobability of cancer score based on the protein measurements and rulingin the likelihood of cancer for the subject if the score in step ishigher than a pre-determined score

In another aspect the invention further provides a method of determiningthe likelihood of the presence of a lung condition in a subject bymeasuring an abundance of panel of proteins in a sample obtained fromthe subject, calculating a probability of cancer score based on theprotein measurements and concluding the presence of said lung conditionif the score is equal or greater than a pre-determined score. The lungcondition is lung cancer such as for example, non-small cell lung cancer(NSCLC). The subject at risk of developing lung cancer

The panel includes at least 4 proteins selected from ALDOA, FRIL, LG3BP,IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN, PRDX1 and CD14. Optionally,the panel further includes at least one protein selected from BGH3,COIA1, TETN, GRP78, PRDX, FIBA and GSLG1.

Alternatively, the panel includes at least 3 proteins selected fromALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN, PRDX1and CD14. In some embodiments, the panel comprises at least 1, 2, 3, or4 proteins selected from LRP1, COIA1, ALDOA, and LG3BP. In someembodiments, the panel comprises at least 1, 2, 3, 4, 5, 6, 7, or 8proteins selected from LRP1, COIA1, ALDOA, LG3BP, BGH3, PRDX1, TETN, andISLR. In some embodiments, the panel comprises at least 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, or 13 proteins selected from LRP1, COIA1, ALDOA,LG3BP, BGH3, PRDX1, TETN, ISLR, TSP1, GRP78, FRIL, FIBA, GSLG1.

Optionally, the panel includes at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,29, 30, 31, 32, 33, 34, 35, or 36 proteins selected from TSP1, COIA1,ISLR, TETN, FRIL, GRP78, ALDOA, BGH3, LG3BP, LRP1, FIBA, PRDX1, GSLG1,KIT, CD14, EF1A1, TENX, AIFM1, GGH, IBP3, ENPL, ERO1A, 6PGD, ICAM1,PTPA, NCF4, SEM3G, 1433T, RAP2B, MMP9, FOLH1, GSTP1, EF2, RAN, SODM, andDSG2.

Optionally, the panel includes at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 proteinsselected from FRIL, TSP1, LRP1, PRDX1, TETN, TBB3, COIA1, GGH, A1AG1,AIFM1, AMPN, CRP, GSLG1, IBP3, KIT, NRP1, 6PGD, CH10, CLIC1, COF1, CSF1,CYTB, DMKN, DSG2, EREG, ERO1A, FOLH1, ILEU, K1C19, LYOX, MMP7, NCF4,PDIA3, PTGIS, PTPA, RAN, SCF, SEM3G, TBA1B, TCPA, TERA, TIMP1, TNF12,and UGPA.

The subject has or is suspected of having a pulmonary nodule. Thepulmonary nodule has a diameter of less than or equal to 3 cm. In oneembodiment, the pulmonary nodule has a diameter of about 0.8 cm to 2.0cm. The subject may have stage IA lung cancer (i.e., the tumor issmaller than 3 cm).

The score is calculated from a logistic regression model applied to theprotein measurements. For example, the score is determinedasP_(s)=1/[1+exp(−α−Σ_(i=1) ^(N)β_(i)*{hacek over (I)}_(i,s))], where{hacek over (I)}_(i,s) is logarithmically transformed and normalizedintensity of transition i in said sample (s), β_(i) is the correspondinglogistic regression coefficient, α was a panel-specific constant, and Nwas the total number of transitions in said panel.

In various embodiments, the method of the present invention furthercomprises normalizing the protein measurements. For example, the proteinmeasurements are normalized by one or more proteins selected from PEDF,MASP1, GELS, LUM, C163A and PTPRJ.

The biological sample such as for example tissue, blood, plasma, serum,whole blood, urine, saliva, genital secretion, cerebrospinal fluid,sweat and excreta.

In one aspect, the determining the likelihood of cancer is determined bythe sensitivity, specificity, negative predictive value or positivepredictive value associated with the score. The score determined has anegative predictive value (NPV) is at least about 60%, at least 70% orat least 80%.

The measuring step is performed by selected reaction monitoring massspectrometry, using a compound that specifically binds the protein beingdetected or a peptide transition. In one embodiment, the compound thatspecifically binds to the protein being measured is an antibody or anaptamer.

In specific embodiments, the diagnostic methods disclosed herein areused to rule out a treatment protocol for a subject, measuring theabundance of a panel of proteins in a sample obtained from the subject,calculating a probability of cancer score based on the proteinmeasurements and ruling out the treatment protocol for the subject ifthe score determined in the sample is lower than a pre-determined score.In some embodiments the panel contains at least 3 proteins selectedALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN, PRDX1and CD 14.

Optionally, the panel further comprises one or more proteins selectedfrom ERO1A, 6PGD, GSTP1, GGH, PRDX1, CD14, PTPA, ICAM1, FOLH1, SODM,FIBA, GSLG1, RAP2B, or C163A or one or more proteins selected from LRP1,COIA1, TSP1, ALDOA, GRP78, FRIL, LG3BP, BGH3, ISLR, PRDX1, FIBA, orGSLG. In preferred embodiments, the panel contains at least TSP1, LG3BP,LRP1, ALDOA, and COIA1. In more a preferred embodiment, the panelcontains at least TSP1, LRP1, ALDOA and COIA1.

In specific embodiments, the diagnostic methods disclosed herein areused to rule in a treatment protocol for a subject by measuring theabundance of a panel of proteins in a sample obtained from the subject,calculating a probability of cancer score based on the proteinmeasurements and ruling in the treatment protocol for the subject if thescore determined in the sample is greater than a pre-determined score.In some embodiments the panel contains at least 3 proteins selectedALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR or TSP1 or ALDOA, FRIL, LG3BP,IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN, PRDX1 and CD14. Optionally,the panel further comprises one or more proteins selected from ERO1A,6PGD, GSTP1, COIA1, GGH, PRDX1, SEM3G, GRP78, TETN, AIFM1, MPRI, TNF12,MMP9 or OSTP or COIA1, TETN, GRP78, APOE or TBB3.

In certain embodiments, the diagnostic methods disclosed herein can beused in combination with other clinical assessment methods, includingfor example various radiographic and/or invasive methods. Similarly, incertain embodiments, the diagnostic methods disclosed herein can be usedto identify candidates for other clinical assessment methods, or toassess the likelihood that a subject will benefit from other clinicalassessment methods.

The high abundance of certain proteins in a biological sample such asplasma or serum can hinder the ability to assay a protein of interest,particularly where the protein of interest is expressed at relativelylow concentrations. Several methods are available to circumvent thisissue, including enrichment, separation, and depletion. Enrichment usesan affinity agent to extract proteins from the sample by class, e.g.,removal of glycosylated proteins by glycocapture. Separation usesmethods such as gel electrophoresis or isoelectric focusing to dividethe sample into multiple fractions that largely do not overlap inprotein content. Depletion typically uses affinity columns to remove themost abundant proteins in blood, such as albumin, by utilizing advancedtechnologies such as IgY14/Supermix (Sigma St. Louis, Mo.) that enablethe removal of the majority of the most abundant proteins.

In certain embodiments of the methods provided herein, a biologicalsample may be subjected to enrichment, separation, and/or depletionprior to assaying biomarker or putative biomarker protein expressionlevels. In certain of these embodiments, blood proteins may be initiallyprocessed by a glycocapture method, which enriches for glycosylatedproteins, allowing quantification assays to detect proteins in the highpg/ml to low ng/ml concentration range. Exemplary methods ofglycocapture are well known in the art (see, e.g., U.S. Pat. No.7,183,188; U.S. Patent Appl. Publ. No. 2007/0099251; U.S. Patent Appl.Publ. No. 2007/0202539; U.S. Patent Appl. Publ. No. 2007/0269895; andU.S. Patent Appl. Publ. No. 2010/0279382). In other embodiments, bloodproteins may be initially processed by a protein depletion method, whichallows for detection of commonly obscured biomarkers in samples byremoving abundant proteins. In one such embodiment, the proteindepletion method is a Supermix (Sigma) depletion method.

In certain embodiments, a biomarker protein panel comprises two to 100biomarker proteins. In certain of these embodiments, the panel comprises2 to 5, 6 to 10, 11 to 15, 16 to 20, 21-25, 5 to 25, 26 to 30, 31 to 40,41 to 50, 25 to 50, 51 to 75, 76 to 100, biomarker proteins. In certainembodiments, a biomarker protein panel comprises one or more subpanelsof biomarker proteins that each comprise at least two biomarkerproteins. For example, biomarker protein panel may comprise a firstsubpanel made up of biomarker proteins that are overexpressed in aparticular lung condition and a second subpanel made up of biomarkerproteins that are under-expressed in a particular lung condition.

In certain embodiments of the methods, compositions, and kits providedherein, a biomarker protein may be a protein that exhibits differentialexpression in conjunction with lung cancer. For example, in certainembodiments a biomarker protein may be one of the proteins associatedwith lung cancer set forth in Table 6.

In other embodiments, the diagnosis methods disclosed herein may be usedto distinguish between two different lung conditions. For example, themethods may be used to classify a lung condition as malignant lungcancer versus benign lung cancer, NSCLC versus SCLC, or lung cancerversus non-cancer condition (e.g., inflammatory condition).

In certain embodiments, kits are provided for diagnosing a lungcondition in a subject. These kits are used to detect expression levelsof one or more biomarker proteins. Optionally, a kit may compriseinstructions for use in the form of a label or a separate insert. Thekits can contain reagents that specifically bind to proteins in thepanels described, herein. These reagents can include antibodies. Thekits can also contain reagents that specifically bind to mRNA expressingproteins in the panels described, herein. These reagents can includenucleotide probes. The kits can also include reagents for the detectionof reagents that specifically bind to the proteins in the panelsdescribed herein. These reagents can include fluorophores.

The following examples are provided to better illustrate the claimedinvention and are not to be interpreted as limiting the scope of theinvention. To the extent that specific materials are mentioned, it ismerely for purposes of illustration and is not intended to limit theinvention. One skilled in the art may develop equivalent means orreactants without the exercise of inventive capacity and withoutdeparting from the scope of the invention

EXAMPLES Example 1 Identification of Lung Cancer Biomarker Proteins

A retrospective, case-control study design was used to identifybiomarker proteins and panels thereof for diagnosing various lungdiseases in pre-defined control and experimental groups. The first goalof these studies was to demonstrate statistically significantdifferential expression for individual proteins between control andexperimental groups. The second goal is to identify a panel of proteinswhich all individually demonstrate statistically significantdifferential expression between control and experimental groups. Thispanel of proteins can then be used collectively to distinguish betweendichotomous disease states.

Specific study comparisons may include 1) cancer vs. non-cancer, 2)small cell lung cancer versus non-small cell lung cancer (NSCLC), 3)cancer vs. inflammatory disease state (e.g., infectious granuloma), or4) different nodule size, e.g., <10 mm versus ≧10 mm (alternativelyusing 10, 15 or 20 mm cut-offs depending upon sample distributions).

Data for each subject consisted of the following:

Archived plasma samples from subjects previously enrolled in InstituteReview Board (IRB)-approved studies was used to identify biomarkerproteins and biomarker panels for distinguishing lung malignancies fromnon-malignancies. Plasma samples were originally obtained by routinephlebotomy, aliquotted, and stored at −80° C. or lower. Samplepreparation, assignment of subject identification codes, initial subjectrecord entry, and specimen storage were performed as per IRB studyprotocols. Sample eligibility is based on clinical parameters, includingthe subject, PN, and clinical staging parameters. Parameters forinclusion and exclusion are set forth in Table 7.

TABLE 7 Inclusion Criteria Sample Sample eligibility will be based onclinical parameters, Inclusion including the following subject, noduleand clinical Criteria staging parameters: Subject age ≧40 any smokingstatus, e.g. current, former, or never co-morbid conditions, e.g. COPDprior malignancy - only skin carcinomas - squamous or basal cell Noduleradiology size ≧4 mm and ≦30 mm solid, semi-solid or non-solid anyspiculation or ground glass opacity pathology malignant - e.g.adenocarcinoma, squamous, or large cell benign - inflammatory (e.g.granulomatous, infectious) or non-inflammatory (e.g. hamartoma)confirmed by biopsy, surgery or stability of lung nodule for 2 years ormore. Clinical stage Primary tumor: ≦T1 (e.g. 1A, 1B) Regional lymphnodes: N0 or N1 only Distant metastasis: M0 only Sample SubjectExclusion prior malignancy within 5 years of lung nodule diagnosisCriteria Nodule size data unavailable for cancer or benign nodule, nopathology or follow-up CT data available Clinical stage Primary tumor:≧T2 Regional lymph nodes: ≧N2 Distant metastasis: ≧M1

The assignment of a sample to a control or experimental group, and itsfurther stratification or matching to other samples within and betweenthese groups, is dependent on various clinical data about the subject.This data includes, for example, demographic information such as age,gender, and clinical history (e.g., smoking status), co-morbidconditions, PN characterization, and pathologic interpretation ofresected lesions and tissues (Table 8).

TABLE 8 1. Enrollment Data a. Demographics - age, birth date, gender,ethnicity b. Measurements - Height (cm) and weight (kg) c. Smokinghistory - never, former, or current with pack-year estima- tion d.Medical history - details of co-morbid conditions, e.g. chronicobstructive pulmonary disease (COPD), inflammatory or autoimmunediseases, endocrine (diabetes), and cardiovascular e. Medicationhistory - current medications, dosages and indications f. Radiographicdata and nodule characteristics 1) nodule size in millimeters (width ×height × length) 2) location, e.g. right or left and upper, lower ormiddle 3) quality, e.g. solid, semi-solid, ground glass, calcified, etc.2. Diagnostic Evaluation Data a. Primary diagnosis and associatedreports (clinical history, physical exam, and laboratory tests report)b. Pulmonary Function Tests (PFTs), if available c. Follow-up CT scans -subsequent nodule evaluations by chest CT d. PET scan e. ClinicalStaging f. Biopsy procedures 1) FNA or TTNA 2) bronchoscopy withtransbronchial or needle biopsy 3) surgical diagnostic procedures, e.g.VATS and/or thoracotomy 3. Radiology Report(s) 4. Pathology Report(s) 5.Blood Sample Collection Information 6. Reporting of Adverse Events a.AEs resulting from center's SOC, e.g. procedural morbidity. Subjectdemographics - e.g. age, gender, ethnicity smoking status - e.g. never-,former- (“ex-”) or current- smoker; pack-years clinical history - e.g.co-morbid conditions, e.g. COPD, infection Nodule size - e.g. planar(width × height × length) and volume dimensions appearance - e.g.calcifications, ground glass appearance, eccentricity Pathology primarylung vs. systemic disorder malignancy status - malignant vs. benign (vs.indeterminate) histopathology - e.g. small cell lung cancer (SCLC) vs.non-small cell lung cancer (NSCLC - adenocarcinoma, squamous carcinoma,large cell carcinoma); other types, e.g. hematologic, carcinoid, etc.immunologically quiescent, e.g. hamartoma, vs. inflammatory, e.g.granulomatous and/or infectious, e.g. fungal

The study design and analytical plan prioritizes thecontrol:experimental group pairings set forth in Table 9. Additionalclinical and molecular insights may be gained by selective inclusion ofphenotypes, e.g. effect of smoking, in the assignment of experimentaland control groups. Demographic information available in the clinicaldatabase will enable further refinements in sample selection via thestratification or matching of samples in the case-control analyses withrespect to clinical parameters, e.g., age and nodule size.

TABLE 9 Assignment of Experimental and Control Groups to AchieveProteomic Analysis Objectives Experimental Analysis Objective GroupControl Group 1 Differentiate A. Cancer Any non- cancer from benignnodule malignant lung nodule (benign) phenotype with nodule ≧4 mm indiameter 2 Differentiate A. Cancer Non-malignant cancer from non- nodule(non-benign) lung malignant disorder, e.g. (inflammatory, granulomatousinfectious) (fungal) disease, lung nodule with nodule

LC-SRM-MS is performed to identify and quantify various plasma proteinsin the plasma samples. Prior to LC-SRM-MS analysis, each sample isdepleted using IgY14/Supermix (Sigma) and then trypsin-digested. Samplesfrom each control or experimental group are batched randomly andprocessed together on a QTrap 5500 instrument (AB SCIEX, Foster City,Calif.) for unbiased comparisons. Each sample analysis takesapproximately 30 minutes. Peak areas for two transitions (native andheavy label) are collected and reported for all peptides and proteins.The data output for each protein analyzed by LC-SRM-MS typically yieldsfour measurements consisting of two transition measurements from each oftwo peptides from the same protein. These measurements enable aninference of the relative abundance of the target protein, which will beused as its expression level in the bioinformatics and statisticalanalyses.

Identification of biomarker proteins having differential expressionlevels between the control and experimental groups yields one or morenovel proteomic profiles. For example, biomarker proteins are identifiedwith expression levels that differ in subjects with PNs who arediagnosed with NSCLC versus those without an NSCLC diagnosis, or insubjects with PNs who are diagnosed with NSCLC versus an inflammatorydisorder. Panels of biomarker proteins are also identified which cancollectively discriminate between dichotomous disease states.

Analyses may be (a priori) powered appropriately to control type 1 andtype 2 errors at 0.05 and to detect inter-cohort differences of 25% peranalyte. The diagnostic power of individual proteins is generallyassessed to distinguish between two cohorts, assuming a one-sided pairednon-parametric test is used. This provides a lower bound on the samplesize required to demonstrate differential expression betweenexperimental and control groups. Multiple testing effects apply for theidentification of panels of proteins for assessing diagnostic efficacy,which requires larger sample sizes.

The sequence of steps for determining statistical significance fordifferential expression of an individual protein includes thefollowing: 1) assessing and correlating the calibrated values oftransitions of a single protein (a quality control measure); 2)comparing paired analysis of groups to control for other influencesusing the Mann-Whitney U-test (rank sum) to determine statisticalsignificance; and 3) determining its significance based on a pre-definedsignificance threshold. Transitions within a protein that are notcorrelated across samples (e.g., Pearson correlation <0.5) will bedeemed unreliable and excluded from the analysis.

Comparison of calibrated samples between two cohorts, e.g., cancer andnon-cancer, requires pairing or matching using a variety of clinicalparameters such as nodule size, age and gender. Such pairing controlsfor the potential influence of these other parameters on the actualcomparison goal, e.g. cancer and non-cancer. A non-parametric test suchas the Mann-Whitney U-test (rank sum) will then be applied to measurethe statistical difference between the groups. The resulting p value canbe adjusted using multiple testing corrections such as the falsediscovery rate. Permutation tests can be used for further significanceassessments.

Significance will be determined by the satisfaction of a pre-definedthreshold, such as 0.05, to filter out assays, with the potential use ofhigher threshold values for additional filtering. An additionalsignificance criterion is that two of three replicate assays mustindividually be significant in order for the assay, e.g., singleprotein, to be significant.

Panels of proteins that individually demonstrate statisticallysignificant differential expression as defined above and which cancollectively be used to distinguish dichotomous disease states areidentified using statistical methods described herein. This requiresdeveloping multivariate classifiers and assessing sensitivity,specificity, and ROC AUC for panels. In addition, protein panels withoptimal discriminatory performance, e.g., ROC AUC, are identified andmay be sufficient for clinical use in discriminating disease states.

The sequence of steps for determining the statistical significance ofthe discriminatory ability of a panel of proteins includes 1) developingmultivariate classifiers for protein panels, and 2) identifying aprotein panel with optimal discriminatory performance, e.g. ROC AUC, fora set of disease states.

A multivariate classifier (e.g., majority rule) will be developed forprotein panels, including single protein assays deemed to besignificant. The sensitivity and specificity of each classifier will bedetermined and used to generate a receiver operating characteristics(ROC) curve and its AUC to assess a given panel's discriminatoryperformance for a specific comparison, e.g. cancer versus non-cancer.

Protocol

1. Review clinical data from a set of subjects presenting with lungdisease.

2. Provide plasma samples from the subjects wherein the samples areeither benign, cancerous, COPD or another lung disease.

3. Group the plasma samples that are benign or cancerous by PNs that areseparated by size of the nodule.

4. Target a pool of 371 putative lung cancer biomarker proteinsconsisting of at least two peptides per protein and at least twoLC-SRM-MS transitions per peptide. Measuring the LC-SRM-MS transitionsin each specimen along with 5 synthetic internal standards consisting of10 transitions to compare peptide transitions from the plasma to thesynthetic internal standards by LC-SRM-MS mass spectroscopy.

5. Quantitate the intensity of each transition.

6. Normalize the quantitated transitions to internal standards to obtaina normalized intensity.

7. Review the measured peptide transitions for correlations from thesame peptide, rejecting discordant transitions.

8. Generate an ROC for each transition by comparing cancerous withbenign samples. (ROC compare specificity (true positive) to(1-sensitivity) false positive).

9. Define the AUC for each transition. (An AUC of 0.5 is a randomclassifier; 1.0 is a perfect classifier).

10. Determine an AUC cut-off point to determine transitions that arestatistically significant.

11. Define the transitions that exceed the AUC cutoff point.

12. Combine all pairings of significant transitions.

13. Define a new AUC for each transition pair by means of logisticalregression.

14. Repeat pairing combinations into triples, quad, etc.; defining a newAUC based upon the logistical regression of combined transitions until apanel of biomarker transitions with combined desired performance(sensitivity & specificity) have been achieved.

15. The panel of biomarker transitions is verified against previouslyunused set of plasma panels.

Example 2 Diagnosis/Classification of Lung Disease Using BiomarkerProteins

Plasma samples will be obtained from one or more subjects presentingwith PNs to evaluate whether the subjects have a lung condition. Theplasma samples will be depleted using IgY14/Supermix (Sigma) andoptionally subjected to one or more rounds of enrichment and/orseparation, and then trypsinized. The expression level of one or morebiomarker proteins previously identified as differentially expressed insubjects with the lung condition will be measured using an LC-SRM-MSassay. The LC-SRM-MS assay will utilize two to five peptide transitionsfor each biomarker protein. For example, the assay may utilize one ormore of the peptide transitions generated from any of the proteinslisted in Table 6. Subjects will be classified as having the lungcondition if one or more of the biomarker proteins exhibit expressionlevels that differ significantly from the pre-determined controlexpression level for that protein.

Example 3 Blood-Based Diagnostic Test to Determine the Likelihood that aPulmonary Nodule (PN) is Benign or Malignant

A panel of 15 proteins was created where the concentration of these 15proteins relative to the concentration of 6 protein standards isindicative of likelihood of cancer. The relative concentration of these15 proteins to the 6 protein standards was measured using a massspectrometry methodology. A classification algorithm is used to combinethese relative concentrations into a relative likelihood of the PN beingbenign or malignant. Further it has been demonstrated that there aremany variations on these panels that are also diagnostic tests for thelikelihood that a PN is benign or malignant. Variations on the panel ofproteins, protein standards, measurement methodology and/orclassification algorithm are described herein.

Study Design

A Single Reaction Monitoring (SRM) mass spectrometry (MS) assay wasdeveloped consisting of 1550 transitions from 345 lung cancer associatedproteins. The SRM-MS assay and methodology is described above. The goalof this study was to develop a blood-based diagnostic for classifyingPNs under 2 cm in size as benign or malignant. The study design appearsin Table 10.

TABLE 10 Study Design Small (<2 cm) Large (>2 cm) Laval UPenn NYU LavalUPenn NYU Benign 14 29 29 13 21 15 Malignant 14 29 29 13 21 15 Batches 12 2 1 2 1 72 vs. 72 (94% power) 49 vs. 49 (74% power)

The study consisted of 242 plasma samples from three sites (Laval, UPennand NYU). The number of benign and malignant samples from each site areindicated in Table 10. The study consisted of 144 plasma samples frompatients with PNs of size 2 cm or less and of 98 samples from patientswith PNs of size larger than 2 cm. This resulted in an estimated powerof 94% for discovering proteins with blood concentrations of 1.5 fold ormore between benign and malignant cancer samples of size 2 cm or less.Power is 74% for PNs of size larger than 2 cm.

This study was a retrospective multisite study that was intended toderive protein biomarkers of lung cancer that are robust to site-to-sitevariation. The study included samples larger than 2 cm to ensure thatproteins not detectable due to the limit of detection of the measurementtechnology (LC-SRM-MS) for tumors of size 2 cm or less could still bedetected in tumors of size 2 cm or larger.

Samples from each site and in each size class (above and below 2 cm)were matched on nodule size, age and gender.

Sample Analysis

Each sample was analyzed using the LC-SRM-MS measurement methodology asfollows:

1. Samples were depleted of high abundance proteins using the IGy14 andSupermix depletion columns from Sigma-Aldrich.

2. Samples were digested using trypsin into tryptic peptides.

3. Samples were analyzed by LC-SRM-MS using a 30 minute gradient on aWaters nanoacuity LC system followed by SRM-MS analysis of the 1550transitions on a AB-Sciex 5500 triple quad device.

4. Raw transition ion counts were obtained and recorded for each of the1550 transitions.

It is important to note that matched samples were processed at each stepeither in parallel (steps 2 and 4) or back-to-back serially (steps 1 and3). This minimizes analytical variation. Finally, steps 1 and 2 of thesample analysis are performed in batches of samples according to day ofprocessing. There were five batches of ‘small’ samples and four batchesof ‘large’ samples as denoted in Table 10.

Protein Shortlist

A shortlist of 68 proteins reproducibly diagnostic across sites wasderived as follows. Note that each protein can be measured by multipletransitions.

Step 1: Normalization

Six proteins were identified that had a transition detected in allsamples of the study and with low coefficient of variation. For eachprotein the transition with highest median intensity across samples wasselected as the representative transition for the protein. Theseproteins and transitions are found in Table 11.

TABLE 11 Normalizing Factors Protein (Uniprot ID)Peptide (Amino Acid Sequence) Transition (m/z) CD44_HUMANYGFIEGHVVIPR (SEQ ID NO: 1) 272.2 TENX_HUMAN YEVTVVSVR (SEQ ID NO: 2)759.5 CLUS_HUMAN ASSIIDELFQDR (SEQ ID NO: 3) 565.3 IBP3_HUMANFLNVLSPR (SEQ ID NO: 4) 685.4 GELS_HUMAN TASDFITK (SEQ ID NO: 5) 710.4MASP1_HUMAN TGVITSPDFPNPYPK (SEQ ID NO: 6) 258.10

We refer to the transitions in Table 11 as normalizing factors (NFs).Each of the 1550 transitions were normalized by each of the sixnormalizing factors where the new intensity of a transition t in asample s by NF f, denoted New(s,t,f), is calculated as follows:

New(s,t,f)=Raw(s,t)*Median(f)/Raw(s,f)

where Raw(s,t) is the original intensity of transition t in sample s;Median(f) is the median intensity of the NF f across all samples; andRaw(s,f) is the original intensity of the NF f in sample s.

For each protein and normalized transition, the AUC of each batch wascalculated. The NF that minimized the coefficient of variation acrossthe 9 batches was selected as the NF for that protein and for alltransitions of that protein. Consequently, every protein (and all of itstransitions) are now normalized by a single NF.

Step 2: Reproducible Diagnostic Proteins

For each normalized transition its AUC for each of the nine batches inthe study is calculated as follows. If the transition is detected infewer than half of the cancer samples and in fewer than half of thebenign samples then the batch AUC is ‘ND’. Otherwise, the batch AUC iscalculated comparing the benign and cancer samples in the batch.

The batch AUC values are transformed into percentile AUC scores for eachtransition. That is, if a normalized transition is in the 82ndpercentile of AUC scores for all transitions then it is assignedpercentile AUC 0.82 for that batch.

Reproducible transitions are those satisfying at least one of thefollowing criteria:

1. In at least four of the five small batches the percentile AUC is 75%or more (or 25% and less).

2. In at least three of the five small batches the percentile AUC is 80%or more (or 20% and less) AND the remaining percentile AUCs in the smallbatches are above 50% (below 50%).

3. In all five small batches the percentile AUC is above 50% (below50%).

4. In at least three of the four large batches the percentile AUC is 85%or more (or 15% and less).

5. In at least three of the four large batches the percentile AUC is 80%or more (or 20% and less) AND the remaining percentile AUCs in the largebatches are above 50% (below 50%).

6. In all four large batches the percentile AUC is above 50% (below50%).

These criteria result in a list of 67 proteins with at least onetransition satisfying one or more of the criteria. These proteins appearin Table 12.

TABLE 12 Percentage Occurrence Occurrence Across131 Across 131 UniprotProtein (Uniprot) Panels Panels Protein Names Accession No. G3P_HUMAN113 86% Glyceraldehyde-3-phosphate P04406 dehydrogenase; Short name =GAPDH; Alternative name(s): Peptidyl-cysteine S-nitrosylase GAPDHFRIL_HUMAN 107 82% Recommended name: P02792 Ferritin light chain Shortname = Ferritin L subunit HYOU1_HUMAN 69 53% Recommended name: Q9Y4L1Hypoxia up-regulated protein 1 Alternative name(s): 150 kDaoxygen-regulated protein Short name = ORP-150 170 kDa glucose-regulatedprotein Short name = GRP-170 ALDOA_HUMAN 66 50% Recommended name: P04075Fructose-bisphosphate aldolase A EC = 4.1.2.13 Alternative name(s): Lungcancer antigen NY-LU-1 Muscle-type aldolase HXK1_HUMAN 65 50%Recommended name: P19367 Hexokinase-1 EC = 2.7.1.1 Alternative name(s):Brain form hexokinase Hexokinase type I Short name = HK I APOE_HUMAN 6348% Recommended name: P02649 Apolipoprotein E Short name = Apo-ETSP1_HUMAN 63 48% Recommended name: P07996 Thrombospondin-1 FINC_HUMAN62 47% Recommended name: P02751 Fibronectin Short name = FN Alternativename(s): Cold-insoluble globulin Short name = CIG Cleaved into thefollowing 4 chains: 1. Anastellin 2. Ugl-Y1 3. Ugl-Y2 4. Ugl-Y3LRP1_HUMAN 58 44% Recommended name: Prolow-density lipoprotein receptor-related protein 1 Short name = LRP-1 Alternative name(s):Alpha-2-macroglobulin receptor Short name = A2MR Apolipoprotein Ereceptor Short name = APOER CD_antigen = CD91 Cleaved into the following3 chains: 1. Low-density lipoprotein receptor-related protein 1 85 kDasubunit Short name = LRP-85 2.Low-density lipoprotein receptor-relatedprotein 1 515 kDa subunit Short name = LRP-515 3. Low-densitylipoprotein receptor-related protein 1 intracellular domain Short name =LRPICD 6PGD_HUMAN 50 38% Recommended name: P52209 6-phosphogluconatedehydrogenase, decarboxylating S10A6_HUMAN 47 36% Recommended name:P06703 Protein S100-A6 Alternative name(s): Calcyclin Growthfactor-inducible protein 2A9 MLN 4 Prolactin receptor-associated proteinShort name = PRA S100 calcium-binding protein A6 CALU_HUMAN 45 34%Recommended name: O43852 Calumenin Alternative name(s): Crocalbin IEFSSP 9302 PRDX1_HUMAN 45 34% Recommended name: Q06830 Peroxiredoxin-1 EC= 1.11.1.15 Alternative name(s): Natural killer cell-enhancing factor AShort name = NKEF-A Proliferation-associated gene protein Short name =PAG Thioredoxin peroxidase 2 Thioredoxin-dependent peroxidereductase 2RAN_HUMAN 45 34% Recommended name: P62826 GTP-binding nuclear proteinRan Alternative name(s): Androgen receptor-associated protein 24 GTPaseRan Ras-like protein TC4 Ras-related nuclear protein CD14_HUMAN 43 33%Recommended name: P08571 Monocyte differentiation antigen CD14Alternative name(s): Myeloid cell-specific leucine-rich glycoproteinCD_antigen = CD14 Cleaved into the following 2 chains: 1. Monocytedifferentiation antigen CD14, urinary form 2. Monocyte differentiationantigen CD14, membrane-bound form AMPN_HUMAN 41 31% Recommended name:P15144 Aminopeptidase N Short name = AP-N Short name = hAPN EC =3.4.11.2 Alternative name(s): Alanyl aminopeptidase Aminopeptidase MShort name = AP-M Microsomal aminopeptidase Myeloid plasma membraneglycoprotein CD13 gp150 CD_antigen = CD13 GSLG1_HUMAN 36 27% Recommendedname: Q92896 Golgi apparatus protein 1 Alternative name(s): CFR-1Cysteine-rich fibroblast growth factor receptor E-selectin ligand 1Short name = ESL-1 Golgi sialoglycoprotein MG-160 1433Z_HUMAN 32 24%Recommended name: P63104 14-3-3 protein zeta/delta Alternative name(s):Protein kinase C inhibitor protein 1 Short name = KCIP-1 IBP3_HUMAN 3124% Recommended name: P17936 Insulin-like growth factor-binding protein3 Short name = IBP-3 Short name = IGF-binding protein 3 Short name =IGFBP-3 ILK_HUMAN 31 24% Recommended name: Q13418 Integrin-linkedprotein kinase EC = 2.7.11.1 Alternative name(s): 59 kDaserine/threonine-protein kinase ILK-1 ILK-2 p59ILK LDHB_HUMAN 30 23%Recommended name: P07195 L-lactate dehydrogenase B chain Short name =LDH-B EC = 1.1.1.27 Alternative name(s): LDH heart subunit Short name =LDH-H Renal carcinoma antigen NY-REN-46 MPRI_HUMAN 29 22% Recommendedname: P11717 Cation-independent mannose-6-phosphate receptor Short name= CI Man-6-P receptor Short name = CI-MPR Short name = M6PR Alternativename(s): 300 kDa mannose 6-phosphate receptor Short name = MPR 300Insulin-like growth factor 2 receptor Insulin-like growth factor IIreceptor Short name = IGF-II receptor M6P/IGF2 receptor Short name =M6P/IGF2R CD_antigen = CD222 PROF1_HUMAN 29 22% Recommended name: P07737Profilin-1 Alternative name(s): Profilin I PEDF_HUMAN 28 21% Recommendedname: P36955 Pigment epithelium-derived factor Short name = PEDFAlternative name(s): Cell proliferation-inducing gene 35 protein EPC-1Serpin F1 CLIC1_HUMAN 26 20% Recommended name: O00299 Chlorideintracellular channel protein 1 Alternative name(s): Chloride channelABP Nuclear chloride ion channel 27 Short name = NCC27 Regulatorynuclear chloride ion channel protein Short name = hRNCC GRP78_HUMAN 2519% Recommended name: P11021 78 kDa glucose-regulated protein Short name= GRP-78 Alternative name(s): Endoplasmic reticulum lumenal Ca(2+)-binding protein grp78 Heat shock 70 kDa protein 5 Immunoglobulin heavychain-binding protein Short name = BiP CEAM8_HUMAN 24 18% Recommendedname: P31997 Carcinoembryonic antigen-related cell adhesion molecule 8Alternative name(s): CD67 antigen Carcinoembryonic antigen CGM6Non-specific cross-reacting antigen NCA-95 CD_antigen = CD66b VTNC_HUMAN24 18% Recommended name: P04004 Vitronectin Alternative name(s):S-protein Serum-spreading factor V75 Cleaved into the following 3chains: 1. Vitronectin V65 subunit 2. Vitronectin V10 subunit 3.Somatomedin-B CERU_HUMAN 22 17% Recommended name: P00450 CeruloplasminEC = 1.16.3.1 Alternative name(s): Ferroxidase DSG2_HUMAN 22 17%Recommended name: Q14126 Desmoglein-2 Alternative name(s): Cadherinfamily member 5 HDGC KIT HUMAN 22 17% Recommended name: P10721 Mast/stemcell growth factor receptor Kit Short name = SCFR EC = 2.7.10.1Alternative name(s): Piebald trait protein Short name = PBTProto-oncogene c-Kit Tyrosine-protein kinase Kit p145 c-kit v-kitHardy-Zuckerman 4 feline sarcoma viral oncogene homolog CD_antigen =CD117 TBB3_HUMAN 22 17% Recommended name: Q13509 Tubulin beta-3 chainAlternative name(s): Tubulin beta-4 chain Tubulin beta-III CH10_HUMAN 2116% Recommended name: P61604 10 kDa heat shock protein, mitochondrialShort name = Hsp10 Alternative name(s): 10 kDa chaperonin Chaperonin 10Short name = CPN10 Early-pregnancy factor Short name = EPF ISLR_HUMAN 2116% Immunoglobulin superfamily containing O14498 leucine-rich repeatprotein MASP1_HUMAN 21 16% Recommended name: P48740 Mannan-bindinglectin serine protease 1 EC = 3.4.21.— Alternative name(s): Complementfactor MASP-3 Complement-activating component of Ra- reactive factorMannose-binding lectin-associated serine protease 1 Short name = MASP-1Mannose-binding protein-associated serine protease Ra-reactive factorserine protease p100 Short name = RaRF Serine protease 5 Cleaved intothe following 2 chains: 1. Mannan-binding lectin serine protease 1 heavychain 2. Mannan-binding lectin serine protease 1 light chain ICAM3_HUMAN20 15% Recommended name: P32942 Intercellular adhesion molecule 3 Shortname = ICAM-3 Alternative name(s): CDw50 ICAM-R CD_antigen = CD50PTPRJ_HUMAN 20 15% Recommended name: Q12913 Receptor-typetyrosine-protein phosphatase eta Short name = Protein-tyrosinephosphatase eta Short name = R-PTP-eta EC = 3.1.3.48 Alternativename(s): Density-enhanced phosphatase 1 Short name = DEP-1 HPTP etaProtein-tyrosine phosphatase receptor type J Short name = R-PTP-JCD_antigen = CD148 A1AG1_HUMAN 19 15% Recommended name: P02763Alpha-1-acid glycoprotein 1 Short name = AGP 1 Alternative name(s):Orosomucoid-1 Short name = OMD 1 CD59_HUMAN 18 14% Recommended name:P13987 CD59 glycoprotein Alternative name(s): 1F5 antigen 20 kDahomologous restriction factor Short name = HRF-20 Short name = HRF20MAC-inhibitory protein Short name = MAC-IP MEM43 antigen Membrane attackcomplex inhibition factor Short name = MACIF Membrane inhibitor ofreactive lysis Short name = MIRL Protectin CD_antigen = CD59 MDHM_HUMAN18 14% commended name: P40926 Malate dehydrogenase, mitochondrialPVR_HUMAN 18 14% Recommended name: P15151 Poliovirus receptorAlternative name(s): Nectin-like protein 5 Short name = NECL-5CD_antigen = CD155 SEM3G_HUMAN 18 14% Recommended name: Q9NS98Semaphorin-3G Alternative name(s): Semaphorin sem2 CO6A3_HUMAN 17 13%Collagen alpha-3(VI) chain P12111 MMP9_HUMAN 17 13% Recommended name:P14780 Matrix metalloproteinase-9 Short name = MMP-9 EC = 3.4.24.35Alternative name(s): 92 kDa gelatinase 92 kDa type IV collagenaseGelatinase B Short name = GELB Cleaved into the following 2 chains: 1.67 kDa matrix metalloproteinase-9 2. 82 kDa matrix metalloproteinase-9TETN_HUMAN 17 13% Recommended name: P05452 Tetranectin Short name = TNAlternative name(s): C-type lectin domain family 3 member B Plasminogenkringle 4-binding protein TNF12_HUMAN 17 13% Recommended name: O43508Tumor necrosis factor ligand superfamily member 12 Alternative name(s):APO3 ligand TNF-related weak inducer of apoptosis Short name = TWEAKCleaved into the following 2 chains: 1. Tumor necrosis factor ligandsuperfamily member 12, membrane form 2. Tumor necrosis factor ligandsuperfamily member 12, secreted form BST1_HUMAN 16 12% Recommended name:Q10588 ADP-ribosyl cyclase 2 EC = 3.2.2.5 Alternative name(s): Bonemarrow stromal antigen 1 Short name = BST-1 Cyclic ADP-ribose hydrolase2 Short name = cADPr hydrolase 2 CD_antigen = CD157 COIA1_HUMAN 16 12%Recommended name: P39060 Collagen alpha-1(XVIII) chain Cleaved into thefollowing chain: 1. Endostatin CRP_HUMAN 16 12% Recommended name: P02741C-reactive protein Cleaved into the following chain: 1. C-reactiveprotein(1-205) PLSL_HUMAN 16 12% Recommended name: P13796 Plastin-2Alternative name(s): L-plastin LC64P Lymphocyte cytosolic protein 1Short name = LCP-1 BGH3_HUMAN 15 11% Recommended name: Q15582Transforming growth factor-beta-induced protein ig-h3 Short name = Betaig-h3 Alternative name(s): Kerato-epithelin RGD-containingcollagen-associated protein Short name = RGD-CAP CD44_HUMAN 15 11%Recommended name: P16070 CD44 antigen Alternative name(s): CDw44 EpicanExtracellular matrix receptor III Short name = ECMR-III GP90 lymphocytehoming/adhesion receptor HUTCH-I Heparan sulfate proteoglycan Hermesantigen Hyaluronate receptor Phagocytic glycoprotein 1 Short name =PGP-1 Phagocytic glycoprotein I Short name = PGP-I CD_antigen = CD44ENOA_HUMAN 15 11% Recommended name: P06733 Alpha-enolase EC = 4.2.1.11Alternative name(s): 2-phospho-D-glycerate hydrolyase C-mycpromoter-binding protein Enolase 1 MBP-1 MPB-1 Non-neural enolase Shortname = NNE Phosphopyruvate hydratase Plasminogen-binding proteinLUM_HUMAN 15 11% SCF_HUMAN 15 11% Recommended name: P21583 Kit ligandAlternative name(s): Mast cell growth factor Short name = MGF Stem cellfactor Short name = SCF c-Kit ligand Cleaved into the followingchain: 1. Soluble KIT ligand Short name = sKITLG UGPA_HUMAN 15 11%Recommended name: Q16851 UTP--glucose-1-phosphate uridylyltransferase EC= 2.7.7.9 Alternative name(s): UDP-glucose pyrophosphorylase Short name= UDPGP Short name = UGPase ENPL_HUMAN 14 11% Recommended name: P14625Endoplasmin Alternative name(s): 94 kDa glucose-regulated protein Shortname = GRP-94 Heat shock protein 90 kDa beta member 1 Tumor rejectionantigen 1 gp96 homolog GDIR2_HUMAN 14 11% Recommended name: P52566 RhoGDP-dissociation inhibitor 2 Short name = Rho GDI 2 Alternative name(s):Ly-GDI Rho-GDI beta GELS_HUMAN 14 11% Recommended name: P06396 GelsolinAlternative name(s): AGEL Actin-depolymerizing factor Short name = ADFBrevin SODM_HUMAN 14 11% Recommended name: P04179 Superoxide dismutase[Mn], mitochondrial TPIS_HUMAN 14 11% Recommended name: P60174Triosephosphate isomerase Short name = TIM EC = 5.3.1.1 Alternativename(s): Triose-phosphate isomerase TENA_HUMAN 13 10% Recommended name:P24821 Tenascin Short name = TN Alternative name(s): Cytotactin GMEM GP150-225 Glioma-associated-extracellular matrix antigen Hexabrachion JIMyotendinous antigen Neuronectin Tenascin-C Short name = TN-C ZA2G_HUMAN13 10% Recommended name: P25311 Zinc-alpha-2-glycoprotein Short name =Zn-alpha-2-GP Short name = Zn-alpha-2-glycoprotein LEG1_HUMAN 11  8%Recommended name: P09382 Galectin-1 Short name = Gal-1 Alternativename(s): 14 kDa laminin-binding protein Short name = HLBP14 14 kDalectin Beta-galactoside-binding lectin L-14-I Galaptin HBL HPLLactose-binding lectin 1 Lectin galactoside-binding soluble 1 PutativeMAPK-activating protein PM12 S-Lac lectin 1 FOLH1_HUMAN 9  7%Recommended name: Q04609 Glutamate carboxypeptidase 2 EC = 3.4.17.21Alternative name(s): Cell growth-inhibiting gene 27 protein Folatehydrolase 1 Folylpoly-gamma-glutamate carboxypeptidase Short name = FGCPGlutamate carboxypeptidase II Short name = GCPII Membrane glutamatecarboxypeptidase Short name = mGCP N-acetylated-alpha-linked acidicdipeptidase I Short name = NAALADase I Prostate-specific membraneantigen Short name = PSM Short name = PSMA Pteroylpoly-gamma-glutamatecarboxypeptidase PLXC1_HUMAN 9  7% PTGIS_HUMAN 9  7% Recommended name:Q16647 Prostacyclin synthase EC = 5.3.99.4 Alternative name(s):Prostaglandin I2 synthase

Step 3: Significance and Occurrence

To find high performing panels, 10,000 trials were performed where oneach trial the combined AUC of a random panel of 15 proteins selectedfrom Table 12 was estimated. To calculate the combined AUC of each panelof 15 proteins, the highest intensity normalized transition wasutilized. Logistic regression was used to calculate the AUC of the panelof 15 across all small samples. 131 panels of 15 proteins had combinedAUC above 0.80, as shown in FIG. 1. (The significance by study separatedinto small (<2.0 cm) and large (>2.0 cm) PN are shown in FIG. 2). Theresilience of the panels persisted despite site based variation in thesamples as shown in FIG. 3. The panels are listed in Table 13.

TABLE 13 AUC P1 P2 P3 P4 P5 P6 P7 0.8282 CD59 CALU LDHB ALDOA DSG2 MDHMTENA 0.8255 CD59 TSP1 KIT ISLR ALDOA DSG2 1433Z 0.8194 S10A6 ALDOA PVRTSP1 CD44 CH10 PEDF 0.8189 ALDOA LEG1 CALU LDHB TETN FOLH1 MASP1 0.8187PVR CD59 CRP ALDOA GRP78 DSG2 6PGD 0.8171 AMPN IBP3 CALU CD44 BGH3 GRP781433Z 0.8171 CALU CH10 ALDOA BST1 MDHM VTNC APOE 0.8165 LDHB CO6A3 CD44A1AG1 GRP78 DSG2 MDHM 0.8163 TPIS CD59 S10A6 CALU ENPL CH10 ALDOA 0.8163LEG1 AMPN S10A6 CALU ISLR ENOA VTNC 0.8161 AMPN S10A6 TSP1 MPRI VTNC LUM6PGD 0.8159 ALDOA AMPN TSP1 BGH3 GRP78 PTPRJ MASP1 0.8159 ALDOA CO6A3MPRI SEM3G CERU LUM APOE 0.8159 AMPN CALU ISLR SODM CERU LUM 6PGD 0.8159CALU PEDF CRP GRP78 VTNC 1433Z CD14 0.8157 TPIS LEG1 S10A6 LDHB TSP1ENPL MDHM 0.8155 CALU CRP ALDOA SODM SEM3G 1433Z FRIL 0.8153 CALU MPRIALDOA PEDF DSG2 CERU APOE 0.814 LEG1 COIA1 AMPN S10A6 TSP1 MPRI PEDF0.8138 TSP1 KIT CERU 6PGD APOE CD14 FRIL 0.8132 S10A6 COIA1 AMPN TSP1PEDF ISLR PTPRJ 0.8128 TPIS LEG1 AMPN S10A6 IBP3 CALU DSG2 0.8128 TPISAMPN TSP1 PEDF A1AG1 MPRI ALDOA 0.8124 ALDOA CALU LDHB PLSL PEDF MASP16PGD 0.8124 AMPN S10A6 TSP1 ENOA GRP78 6PGD APOE 0.812 IBP3 TSP1 CRPA1AG1 SCF ALDOA PEDF 0.8106 COIA1 CALU CD44 BGH3 ALDOA TETN BST1 0.8106TSP1 PLSL CRP ALDOA GRP78 MDHM APOE 0.8099 CD59 CALU ENPL CD44 ALDOATENA 6PGD 0.8097 AMPN S10A6 IBP3 A1AG1 MPRI ALDOA GRP78 0.8093 ALDOAS10A6 TSP1 ENPL PEDF A1AG1 GRP78 0.8093 PVR IBP3 LDHB SCF TNF12 LUM1433Z 0.8093 CALU LDHB CO6A3 PEDF CH10 BGH3 PTPRJ 0.8087 ALDOA AMPN ENPLKIT MPRI GRP78 LUM 0.8087 CD59 S10A6 IBP3 TSP1 ENPL SODM MDHM 0.8083ALDOA AMPN S10A6 IBP3 PLSL CRP SCF 0.8081 PVR IBP3 TSP1 CRP ALDOA SODMMDHM 0.8081 S10A6 LDHB ENPL PLSL CH10 CERU FRIL 0.8081 IBP3 LDHB PEDFMPRI SEM3G VTNC APOE 0.8079 ALDOA AMPN CALU PLSL PEDF CH10 MASP1 0.8077S10A6 IBP3 LDHB MDHM ZA2G FRIL G3P 0.8077 CD59 S10A6 LDHB TSP1 CD44 ISLRCERU 0.8077 AMPN CALU LDHB TSP1 PLSL CD44 ALDOA 0.8075 TPIS AMPN S10A6TSP1 CH10 COIA1 CERU 0.8073 CALU PEDF MPRI ISLR BGH3 ENOA CERU 0.8071TPIS CALU CO6A3 KIT DSG2 MASP1 6PGD 0.8071 LEG1 COIA1 TSP1 CD44 MPRIALDOA FOLH1 0.8065 AMPN S10A6 CALU CO6A3 TSP1 PLSL KIT 0.8063 S10A6 TSP1A1AG1 BGH3 ZA2G 1433Z FRIL 0.8063 CALU KIT ENOA 6PGD APOE CD14 G3P0.8061 AMPN MPRI GRP78 DSG2 TENA APOE CD14 0.8059 TPIS IBP3 TSP1 PEDFTNF12 1433Z 6PGD 0.8059 CALU LDHB PLSL CRP PEDF SEM3G MDHM 0.8058 ALDOATSP1 PLSL CD44 KIT CRP ISLR 0.8058 TPIS TSP1 MPRI ISLR ALDOA PEDF GRP780.8054 ALDOA S10A6 CALU CRP A1AG1 VTNC TENA 0.8054 TPIS CO6A3 TSP1 MPRIDSG2 TNF12 FRIL 0.8054 CALU LDHB DSG2 1433Z CD14 FRIL G3P 0.805 CALUMPRI ENOA FOLH1 LUM ZA2G APOE 0.8048 PVR S10A6 IBP3 PEDF ALDOA BST1 MDHM0.8048 AMPN CALU CH10 DSG2 TNF12 CERU 6PGD 0.8046 ALDOA LDHB TSP1 KITISLR DSG2 MASP1 0.8046 ALDOA COIA1 CD59 IBP3 PTPRJ SEM3G CERU 0.8046 PVRCD59 S10A6 PLSL PEDF CH10 SCF 0.8046 COIA1 IBP3 MASP1 DSG2 TENA ZA2G1433Z 0.8042 BGH3 CD59 CALU LDHB CO6A3 SODM TENA 0.8042 IBP3 TSP1 ENPLCH10 CD14 FRIL G3P 0.8042 IBP3 TSP1 KIT ZA2G 6PGD APOE CD14 0.804 TPISBGH3 S10A6 LDHB CO6A3 CH10 PEDF 0.804 CALU LDHB BGH3 TETN FOLH1 TNF12VTNC 0.8038 TPIS PVR COIA1 CALU SCF MPRI ALDOA 0.8036 S10A6 TPIS COIA1CD59 CO6A3 TSP1 MPRI 0.8036 LEG1 CD59 AMPN CALU CH10 GRP78 SEM3G 0.8036AMPN S10A6 TSP1 ENPL PEDF SODM FOLH1 0.8036 S10A6 CALU MASP1 A1AG1 MPRIALDOA VTNC 0.8036 IBP3 CALU PLSL CD44 KIT CERU 6PGD 0.8036 TSP1 PLSLFOLH1 COIA1 TNF12 VTNC 6PGD 0.8034 ALDOA BGH3 CD59 TSP1 KIT CH10 SODM0.8034 S10A6 CALU LDHB TSP1 GRP78 1433Z 6PGD 0.8032 S10A6 CALU TSP1 KITCH10 PEDF GRP78 0.8032 TSP1 MASP1 CRP ALDOA GRP78 TETN TNF12 0.803 AMPNTSP1 KIT MPRI SEM3G TETN DSG2 0.803 CALU CO6A3 PLSL A1AG1 ALDOA GRP786PGD 0.8028 COIA1 CD59 AMPN TSP1 KIT ISLR ALDOA 0.8024 S10A6 CD44 SCFMPRI ISLR ALDOA APOE 0.8024 S10A6 TSP1 ALDOA SODM ENOA BST1 FRIL 0.8024IBP3 TSP1 SCF ALDOA SODM DSG2 VTNC 0.802 ALDOA TSP1 PLSL CD44 CH10 A1AG1ENOA 0.802 LEG1 CALU LDHB TSP1 CH10 ALDOA MDHM 0.802 CD59 IBP3 TSP1A1AG1 MPRI PTPRJ 6PGD 0.802 IBP3 TSP1 CRP BST1 TNF12 VTNC 1433Z 0.8018LEG1 S10A6 IBP3 CALU TSP1 MASP1 A1AG1 0.8018 COIA1 CD59 AMPN CALU MASP1BST1 VTNC 0.8018 AMPN ALDOA SODM GRP78 MDHM VTNC 6PGD 0.8018 LDHB CO6A3ALDOA SEM3G DSG2 6PGD APOE 0.8016 S10A6 LDHB SCF MPRI ALDOA PEDF ENOA0.8016 LDHB CO6A3 TSP1 1433Z APOE CD14 FRIL 0.8014 ALDOA PEDF MPRI ISLRFOLH1 TNF12 MASP1 0.8014 COIA1 PEDF CRP A1AG1 ENOA CERU FRIL 0.8014 CD59IBP3 TSP1 KIT MASP1 ENOA TNF12 0.8014 LDHB KIT SCF BGH3 SEM3G VTNC 1433Z0.8013 PVR AMPN LDHB CD44 DSG2 TETN MDHM 0.8013 S10A6 LDHB TSP1 ISLR LUMG3P HYOU1 0.8013 CALU A1AG1 MPRI ALDOA PEDF DSG2 VTNC 0.8013 TSP1 ENPLKIT SODM SEM3G DSG2 TETN 0.8013 TSP1 PLSL ISLR ALDOA ENOA MDHM APOE0.8011 ALDOA AMPN CO6A3 SEM3G APOE CD14 FRIL 0.8011 TPIS BGH3 AMPN S10A6CALU LDHB KIT 0.8011 COIA1 IBP3 TSP1 A1AG1 TETN DSG2 6PGD 0.8011 AMPNS10A6 IBP3 CALU KIT SCF ALDOA 0.8011 IBP3 A1AG1 PEDF SEM3G MDHM TNF12VTNC 0.8009 ALDOA BGH3 AMPN LDHB TSP1 PLSL MPRI 0.8009 LEG1 COIA1 IBP3CH10 MASP1 SCF ALDOA 0.8009 AMPN ENPL ALDOA TETN FOLH1 BST1 ZA2G 0.8009CALU CO6A3 ENPL ALDOA GRP78 PTPRJ VTNC 0.8009 TSP1 CH10 PTPRJ TETN TNF12VTNC TENA 0.8007 CD59 S10A6 IBP3 CO6A3 TSP1 KIT ISLR 0.8007 AMPN TSP1KIT SCF TETN ZA2G 1433Z 0.8007 S10A6 IBP3 TSP1 CD44 PEDF A1AG1 PTPRJ0.8007 CALU CO6A3 TSP1 CH10 SCF BGH3 ALDOA 0.8007 ENPL CD44 MASP1 GRP781433Z CD14 FRIL 0.8005 TPIS LEG1 LDHB TSP1 MASP1 A1AG1 MPRI 0.8005 PEDFCRP ISLR ALDOA GRP78 PTPRJ ZA2G 0.8003 ALDOA S10A6 CALU CRP BGH3 TETN6PGD 0.8003 AMPN TSP1 A1AG1 MPRI ISLR ALDOA MASP1 0.8003 CO6A3 TSP1 SCFMPRI ISLR FOLH1 1433Z 0.8001 S10A6 IBP3 TSP1 KIT TETN COIA1 CERU 0.8001S10A6 CALU CH10 ISLR ALDOA SODM PTPRJ 0.8001 IBP3 TSP1 ENPL CH10 CRPISLR ALDOA 0.8001 IBP3 TSP1 PTPRJ ALDOA BST1 LUM 1433Z 0.8001 LDHB TSP1MPRI GRP78 SEM3G LUM ZA2G AUC P8 P9 P10 P11 P12 P13 P14 P15 0.8282 6PGDAPOE FRIL G3P HYOU1 LRP1 RAN HXK1 0.8255 CD14 FRIL HYOU1 LRP1 PROF1 TBB3FINC CEAM8 0.8194 APOE FRIL G3P HYOU1 LRP1 TBB3 CLIC1 RAN 0.8189 1433ZAPOE G3P HYOU1 PRDX1 PROF1 ILK HXK1 0.8187 CD14 FRIL G3P PRDX1 ILK FINCGSLG1 HXK1 0.8171 6PGD CD14 FRIL G3P LRP1 TBB3 FINC RAN 0.8171 CD14 FRILG3P ICAM3 PRDX1 PROF1 PVR HXK1 0.8165 VTNC 1433Z FRIL G3P S10A6 FINCGSLG1 HXK1 0.8163 DSG2 6PGD FRIL G3P HYOU1 ICAM3 PRDX1 FINC 0.8163 6PGDAPOE G3P LRP1 UGPA RAN CEAM8 HXK1 0.8161 APOE CD14 FRIL G3P LRP1 PROF1RAN CEAM8 0.8159 CERU 6PGD FRIL G3P HYOU1 LRP1 PRDX1 CEAM8 0.8159 CD14FRIL G3P LRP1 TBB3 FINC GSLG1 HXK1 0.8159 APOE CD14 FRIL G3P PRDX1 CLIC1ILK HXK1 0.8159 FRIL G3P TBB3 ILK GELS FINC RAN GSLG1 0.8157 6PGD APOEFRIL G3P HYOU1 CLIC1 ILK HXK1 0.8155 G3P HYOU1 LRP1 PRDX1 PROF1 FINC RANGSLG1 0.8153 G3P HYOU1 PLXC1 PRDX1 ILK CEAM8 HXK1 BST1 0.814 GRP78 CERUFRIL G3P PLXC1 PRDX1 ILK HXK1 0.8138 G3P HYOU1 PLXC1 RAN CEAM8 HXK1 BST1MMP9 0.8132 CERU 6PGD CD14 FRIL HYOU1 FINC GSLG1 BST1 0.8128 PTPRJ BST16PGD G3P HYOU1 ILK FINC HXK1 0.8128 VTNC 1433Z APOE FRIL G3P LRP1 PTGISRAN 0.8124 APOE CD14 FRIL G3P GDIR2 FINC GSLG1 HXK1 0.8124 FRIL GDIR2LRP1 CLIC1 FINC GSLG1 HXK1 BST1 0.812 DSG2 1433Z APOE FRIL LRP1 PRDX1PROF1 FINC 0.8106 LUM 1433Z 6PGD FRIL G3P HYOU1 PRDX1 CLIC1 0.8106 FRILG3P PRDX1 UGPA ILK CEAM8 GSLG1 HXK1 0.8099 FRIL G3P HYOU1 PRDX1 PROF1FINC GSLG1 HXK1 0.8097 FRIL G3P HYOU1 LRP1 PTGIS ILK FINC MMP9 0.8093APOE CD14 FRIL G3P LRP1 PLXC1 CLIC1 GSLG1 0.8093 FRIL G3P GDIR2 PRDX1UGPA CLIC1 FINC HXK1 0.8093 ALDOA SEM3G MASP1 G3P HYOU1 FINC CEAM8 HXK10.8087 1433Z 6PGD CD14 FRIL HYOU1 TBB3 CLIC1 FINC 0.8087 6PGD FRIL G3PHYOU1 LRP1 FINC CEAM8 HXK1 0.8083 MPRI GRP78 CERU CD14 FRIL LRP1 FINCCEAM8 0.8081 TNF12 TENA FRIL G3P HYOU1 PROF1 RAN HXK1 0.8081 G3P HYOU1ICAM3 PLXC1 CLIC1 ILK FINC GSLG1 0.8081 CD14 FRIL G3P HYOU1 S10A6 CEAM8GSLG1 HXK1 0.8079 TNF12 LUM 6PGD APOE FRIL HYOU1 RAN HXK1 0.8077 HYOU1LRP1 PTGIS CLIC1 FINC RAN GSLG1 MMP9 0.8077 1433Z FRIL G3P HYOU1 LRP1ILK GSLG1 HXK1 0.8077 TETN APOE CD14 FRIL G3P LRP1 PRDX1 GSLG1 0.8075ZA2G 6PGD FRIL G3P LRP1 UGPA ILK HXK1 0.8073 1433Z 6PGD FRIL G3P HYOU1LRP1 PRDX1 FINC 0.8071 APOE CD14 FRIL G3P LRP1 AMPN RAN HXK1 0.8071TNF12 APOE FRIL HYOU1 LRP1 PTGIS CLIC1 AMPN 0.8065 MASP1 ALDOA APOE FRILG3P TBB3 RAN HXK1 0.8063 G3P LRP1 PROF1 TBB3 UGPA CLIC1 AMPN RAN 0.8063ICAM3 LRP1 PLXC1 PROF1 FINC RAN HXK1 MMP9 0.8061 FRIL G3P LRP1 PLXC1PROF1 PVR FINC CEAM8 0.8059 APOE CD14 FRIL G3P LRP1 TBB3 RAN GSLG10.8059 APOE G3P HYOU1 PRDX1 TBB3 ILK RAN HXK1 0.8058 TNF12 APOE CD14FRIL G3P HYOU1 RAN HXK1 0.8058 SEM3G FRIL G3P HYOU1 PROF1 GELS PVR RAN0.8054 ZA2G 6PGD FRIL G3P HYOU1 ILK GSLG1 HXK1 0.8054 G3P HYOU1 ICAM3PLXC1 TBB3 GELS RAN BST1 0.8054 HYOU1 PLXC1 PRDX1 PROF1 FINC CEAM8 GSLG1MMP9 0.805 CD14 G3P HYOU1 ICAM3 PRDX1 UGPA ILK HXK1 0.8048 VTNC CD14FRIL G3P HYOU1 PTGIS FINC RAN 0.8048 APOE FRIL G3P LRP1 PRDX1 UGPA RANCEAM8 0.8046 1433Z FRIL G3P GDIR2 HYOU1 RAN GSLG1 HXK1 0.8046 CD14 FRILG3P LRP1 PRDX1 FINC GSLG1 MMP9 0.8046 BST1 FRIL G3P CLIC1 ILK AMPN FINCHXK1 0.8046 APOE CD14 FRIL G3P ICAM3 AMPN FINC HXK1 0.8042 APOE G3PHYOU1 S10A6 ILK FINC RAN HXK1 0.8042 HYOU1 ICAM3 LRP1 PRDX1 PROF1 GELSFINC GSLG1 0.8042 FRIL GDIR2 HYOU1 LRP1 PRDX1 PROF1 CLIC1 HXK1 0.804TENA FRIL G3P HYOU1 LRP1 PRDX1 ILK GSLG1 0.804 FRIL G3P GDIR2 PRDX1CLIC1 GELS FINC HXK1 0.8038 ENOA MASP1 APOE FRIL G3P PRDX1 FINC HXK10.8036 ALDOA ENOA 6PGD FRIL G3P GDIR2 LRP1 PRDX1 0.8036 TETN APOE G3PHYOU1 ICAM3 RAN CEAM8 HXK1 0.8036 6PGD APOE FRIL G3P HYOU1 LRP1 HXK1MMP9 0.8036 TENA FRIL G3P PROF1 PTGIS FINC CEAM8 HXK1 0.8036 CD14 FRILG3P HYOU1 PRDX1 FINC CEAM8 HXK1 0.8036 FRIL G3P LRP1 PRDX1 PROF1 GELSFINC RAN 0.8034 VTNC TENA 6PGD G3P HYOU1 LRP1 TBB3 ILK 0.8034 G3P HYOU1ICAM3 PROF1 ILK GELS AMPN FINC 0.8032 SEM3G MASP1 6PGD CD14 FRIL G3PHYOU1 ILK 0.8032 1433Z APOE CD14 G3P HYOU1 PVR RAN HXK1 0.803 1433Z APOEFRIL G3P TBB3 UGPA PVR RAN 0.803 APOE CD14 FRIL G3P HYOU1 ICAM3 PRDX1RAN 0.8028 MDHM CERU LUM ZA2G APOE FRIL LRP1 MMP9 0.8024 FRIL G3P HYOU1PRDX1 GELS FINC CEAM8 HXK1 0.8024 HYOU1 LRP1 PROF1 CLIC1 GELS FINC CEAM8GSLG1 0.8024 1433Z APOE FRIL G3P LRP1 PRDX1 UGPA PTPRJ 0.802 TETN TENAAPOE FRIL G3P TBB3 AMPN GSLG1 0.802 APOE FRIL G3P HYOU1 ILK PVR GSLG1PTPRJ 0.802 APOE FRIL G3P LRP1 ILK RAN CEAM8 MMP9 0.802 FRIL G3P GDIR2HYOU1 LRP1 PRDX1 TBB3 FINC 0.8018 SCF ALDOA SEM3G VTNC FRIL G3P LRP1CLIC1 0.8018 CERU 6PGD APOE CD14 FRIL HYOU1 PROF1 GSLG1 0.8018 FRIL G3PHYOU1 LRP1 PTGIS GELS FINC RAN 0.8018 FRIL G3P HYOU1 ICAM3 PROF1 FINCPTPRJ HXK1 0.8016 SEM3G APOE FRIL G3P HYOU1 PRDX1 CLIC1 GSLG1 0.8016 G3PHYOU1 PROF1 UGPA CLIC1 RAN CEAM8 PTPRJ 0.8014 CERU 6PGD FRIL G3P HYOU1PRDX1 FINC HXK1 0.8014 G3P GDIR2 LRP1 S10A6 GELS FINC GSLG1 HXK1 0.8014CD14 FRIL G3P PRDX1 UGPA FINC PTPRJ HXK1 0.8014 FRIL G3P HYOU1 LRP1PRDX1 PROF1 FINC HXK1 0.8013 FRIL G3P LRP1 PRDX1 ILK FINC HXK1 MMP90.8013 ICAM3 LRP1 PROF1 UGPA ILK FINC PTPRJ HXK1 0.8013 ZA2G 6PGD FRILG3P CLIC1 S10A6 ILK PVR 0.8013 LUM APOE FRIL G3P HYOU1 CLIC1 RAN HXK10.8013 G3P GDIR2 LRP1 PTGIS FINC RAN HXK1 MMP9 0.8011 G3P GDIR2 HYOU1ICAM3 PRDX1 FINC HXK1 MMP9 0.8011 TENA 6PGD APOE G3P LRP1 PROF1 GELSMMP9 0.8011 FRIL GDIR2 HYOU1 LRP1 CLIC1 S10A6 PVR GSLG1 0.8011 APOE G3PICAM3 LRP1 GELS FINC RAN CEAM8 0.8011 1433Z G3P HYOU1 PRDX1 FINC GSLG1PTPRJ HXK1 0.8009 ISLR APOE FRIL LRP1 PVR FINC RAN PTPRJ 0.8009 TNF12CERU APOE CD14 FRIL TBB3 ILK FINC 0.8009 6PGD CD14 FRIL CLIC1 S10A6 ILKFINC MMP9 0.8009 APOE CD14 G3P TBB3 CLIC1 GELS RAN HXK1 0.8009 1433Z6PGD FRIL G3P HYOU1 RAN HXK1 MMP9 0.8007 GRP78 MDHM CD14 FRIL G3P HYOU1GSLG1 HXK1 0.8007 6PGD APOE G3P GDIR2 LRP1 PRDX1 TBB3 RAN 0.8007 SODMCERU APOE FRIL ICAM3 LRP1 UGPA GSLG1 0.8007 ENOA TETN LUM APOE FRIL G3PRAN HXK1 0.8007 G3P GDIR2 ICAM3 LRP1 PRDX1 PROF1 FINC HXK1 0.8005 ALDOAENOA FRIL G3P LRP1 UGPA ILK FINC 0.8005 6PGD G3P HYOU1 PRDX1 TBB3 FINCRAN CEAM8 0.8003 CD14 FRIL G3P CLIC1 FINC GSLG1 HXK1 MMP9 0.8003 LUM6PGD APOE FRIL ICAM3 TBB3 GSLG1 BST1 0.8003 APOE G3P HYOU1 ICAM3 PRDX1UGPA RAN HXK1 0.8001 6PGD CD14 FRIL G3P PROF1 FINC HXK1 MMP9 0.8001 MDHMVTNC FRIL G3P CLIC1 ILK AMPN HXK1 0.8001 SODM 1433Z G3P HYOU1 LRP1 PRDX1PROF1 CEAM8 0.8001 APOE G3P HYOU1 LRP1 PTGIS TBB3 PVR RAN 0.8001 FRILG3P ICAM3 PROF1 TBB3 FINC RAN GSLG1

To calculate the combined AUC of each panel of 15 proteins, the highestintensity normalized transition was utilized. Logistic regression wasused to calculate the AUC of the panel of 15 across all small samples. 5panels of 15 proteins had combined AUC above 0.80.

Finally, the frequency of each of the 67 proteins on the 131 panelslisted in Table 13 is presented in Table 12 both as raw counts (column2) and percentage (column 3). It is an important observation that thepanel size of 15 was pre-selected to prove that there are diagnosticproteins and panels. Furthermore, there are numerous such panels.Smaller panels selected from the list of 67 proteins can also be formedand can be generated using the same methods here.

Example 4 A Diagnostic Panel of 15 Proteins for Determining theProbability that a Blood Sample from a Patient with a PN of Size 2 cm orLess is Benign or Malignant

In Table 14 a logistic regression classifier trained on all smallsamples is presented.

TABLE 14 Transition Normalized Logistic column By column RegressionProtein Transition SEQ ID NO: Normalized By SEQ ID NO: CoefficientALDOA_HUMAN ALQASALK_401.25_617.40 7 YGFIEGHVVIPR_462.92_272.20 1−1.96079 BGH3_HUMAN LTLLAPLNSVFK_658.40_804.50 8 YEVTVVSVR_526.29_759.502 2.21074 CLIC1_HUMAN LAALNPESNTAGLDIFAK_922.99_256.20 9ASSIIDELFQDR_465.24_565.30 3 0.88028 CO6A3_HUMAN VAVVQYSDR_518.77_767.4010 ASSIIDELFQDR_465.24_565.30 3 −1.52046 COIA1_HUMANAVGLAGTFR_446.26_721.40 11 YGFIEGHVVIPR_462.92_272.20 1 −0.76786FINC_HUMAN VPGTSTSATLTGLTR_487.94_446.30 12 FLNVLSPR_473.28_685.40 40.98842 G3P_HUMAN GALQNIIPASTGAAK_706.40_815.50 13TASDFITK_441.73_710.40 5 0.58843 ISLR_HUMAN ALPGTPVASSQPR_640.85_841.5014 FLNVLSPR_473.28_685.40 4 1.02005 LRP1_HUMANTVLWPNGLSLDIPAGR_855.00_400.20 15 YEVTVVSVR_526.29_759.50 2 −2.14383PRDX1_HUMAN QITVNDLPVGR_606.30_428.30 16 YGFIEGHVVIPR_462.92_272.20 1−1.38044 PROF1_HUMAN STGGAPTFNVTVTK_690.40_503.80 17TASDFITK_441.73_710.40 5 −1.78666 PVR_HUMAN SVDIWLR_444.75_702.40 18TASDFITK_441.73_710.40 5 2.26338 TBB3_HUMAN ISVYYNEASSHK_466.60_458.2019 FLNVLSPR_473.28_685.40 4 −0.46786 TETN_HUMANLDTLAQEVALLK_657.39_330.20 20 TASDFITK_441.73_710.40 5 −1.99972TPIS_HUMAN VVFEQTK_425.74_652.30 21 YGFIEGHVVIPR_462.92_272.20 1 2.65334Constant (C₀) 21.9997

The classifier has the structure

${Probability} = \frac{\exp (W)}{1 + {\exp (W)}}$$W = {C_{0} + {\sum\limits_{i = 1}^{15}{C_{i}*P_{i}}}}$

Where C₀ and C_(i) are logistic regression coefficients, P_(i) arelogarithmically transformed normalized transition intensities. Samplesare predicted as cancer if Probability ≧0.5 or as benign otherwise. InTable 14 the coefficients C_(i) appear in the sixth column, C₀ in thelast row, and the normalized transitions for each protein are defined bycolumn 2 (protein transition) and column 4 (the normalizing factor).

The performance of this classifier, presented as a ROC plot, appears inFIG. 4. Overall AUC is 0.81. The performance can also be assessed byapplying the classifier to each study site individually which yields thethree ROC plots appearing in FIG. 5. The resulting AUCs are 0.79, 0.88and 0.78 for Laval, NYU and UPenn, respectively.

Example 5 The Program “Ingenuity”® was Used to Query the Blood Proteinsthat are Used to Identify Lung Cancer in Patients with Nodules that wereIdentified Using the Methods of the Present Invention

Using a subset of 35 proteins (Table 15) from the 67 proteins identifiedas a diagnostic panel (Table 13), a backward systems analysis wasperformed. Two networks were queried that are identified as cancernetworks with the identified 35 proteins. The results show that thenetworks that have the highest percentage of “hits” when the proteinsare queried that are found in the blood of patients down to the level ofthe nucleus are initiated by transcription factors that are regulated byeither cigarette smoke or lung cancer among others. See also Table 16and FIG. 6.

These results are further evidence that the proteins that wereidentified using the methods of the invention as diagnostic for lungcancer are prognostic and relevant.

TABLE 15 No. Protein Protein Name Gene Symbol Gene Name 1 6PGD_HUMAN6-phosphogluconate PGD phosphogluconate dehydrogenase dehydrogenase,decar- boxylating 2 AIFM1_HUMAN Apoptosis-inducing AIFM1apoptosis-inducing factor, mito- factor 1, mitochondrialchondrion-associated, 1 3 ALDOA_HUMAN Fructose-bisphosphate ALDOAaldolase A, fructose-bisphosphate aldolase A 4 BGH3_HUMAN Transforminggrowth TGFBI transforming growth factor, beta- factor-beta-inducedinduced, 68 kDa protein ig-h3 5 C163A_HUMAN Scavenger receptor CD163CD163 molecule cysteine-rich type 1 protein M130 6 CD14_HUMAN Monocytedifferentia- CD14 CD14 molecule tion antigen CD14 7 COIA1_HUMAN Collagenalpha- COL18A1 collagen, type XVIII, alpha 1 1(XVIII) chain 8ERO1A_HUMAN ERO1-like protein ERO1L ERO1-like (S. cerevisiae) alpha 9FIBA_HUMAN Fibrinogen alpha chain FGA fibrinogen alpha chain 10FINC_HUMAN Fibronectin FN1 fibronectin 1 11 FOLH1_HUMAN Glutamatecarboxy- FOLH1 folate hydrolase (prostate-specific peptidase 2 membraneantigen) 1 12 FRIL_HUMAN Ferritin light chain FTL ferritin, lightpolypeptide 13 GELS_HUMAN Gelsolin GSN gelsolin (amyloidosis, Finnishtype) 14 GGH_HUMAN Gamma-glutamyl GGH gamma-glutamyl hydrolase (con-hydrolase jugase, folylpolygammaglutamyl hydrolase) 15 GRP78_HUMAN 78kDa glucose- HSPA5 heat shock 70 kDa protein 5 (glu- regulated proteincose-regulated protein, 78 kDa) 16 GSLG1_HUMAN Golgi apparatus proteinGLG1 golgi apparatus protein 1 1 17 GSTP1_HUMAN Glutathione S- GSTP1glutathione S-transferase pi 1 transferase P 18 IBP3_HUMAN Insulin-likegrowth IGFBP3 insulin-like growth factor binding factor-binding protein3 protein 3 19 ICAM1_HUMAN Intercellular adhesion ICAM1 intercellularadhesion molecule 1 molecule 1 20 ISLR_HUMAN Immunoglobulin super- ISLRimmunoglobulin superfamily family containing leu- containingleucine-rich repeat cine-rich repeat protein 21 LG3BP_HUMANGalectin-3-binding LGALS3BP lectin, galactoside-binding, proteinsoluble, 3 binding protein 22 LRP1_HUMAN Prolow-density lipo- LRP1 lowdensity lipoprotein-related protein receptor-related protein 1(alpha-2-macroglobulin protein 1 receptor) 23 LUM_HUMAN Lumican LUMlumican 24 MASP1_HUMAN Mannan-binding lectin MASP1 mannan-binding lectinserine pep- serine protease 1 tidase 1 (C4/C2 activating com- ponent ofRa-reactive factor) 25 PDIA3_HUMAN Protein disulfide- PDIA3 proteindisulfide isomerase family isomerase A3 A, member 3 26 PEDF_HUMANPigment epithelium- SERPINF1 serpin peptidase inhibitor, clade F derivedfactor (alpha-2 antiplasmin, pigment epithelium derived factor), mem-ber 1 27 PRDX1_HUMAN Peroxiredoxin-1 PRDX1 peroxiredoxin 1 28PROF1_HUMAN Profilin-1 PFN1 profilin 1 29 PTPA_HUMAN Serine/threonine-PPP2R4 protein phosphatase 2A activator, protein phosphatase 2Aregulatory subunit 4 activator 30 PTPRJ_HUMAN Receptor-type tyrosine-PTPRJ protein tyrosine phosphatase, protein phosphatase eta receptortype, J 31 RAP2B_HUMAN Ras-related protein RAP2B RAP2B, member of RASonco- Rap-2b gene family 32 SEM3G_HUMAN Semaphorin-3G SEMA3G semadomain, immunoglobulin domain (Ig), short basic domain, secreted,(semaphorin) 3G 33 SODM_HUMAN Superoxide dismutase SOD2 superoxidedismutase 2, mito- [Mn], mitochondrial chondrial 34 TETN_HUMANTetranectin CLEC3B C-type lectin domain family 3, member B 35 TSP1_HUMANThrombospondin-1 THBS1 thrombospondin 1

TABLE 16 Gene Lung Cancer PubMed Name Protein Associations SamplePublications NFE2L2 nuclear 92 Cigarette Smoking Blocks the Protective(NRF2) factor transcription Expression of Nrf2/ARE Pathway . . .(erythroid- factor Molecular mechanisms for the regulation derived 2)-protecting of Nrf2-mediated cell proliferation in non- like 2 cell fromsmall-cell lung cancers . . . oxidative stress EGR1 early 38 Cigarettesmoke-induced Egr-1 upregulates growth transcription proinflammatorycytokines in pulmonary response factor epithelial cells . . . involvedEGR-1 regulates Ho-1 expression induced oxidative stress by cigarettesmoke . . . Chronic hypoxia induces Egr-1 via activa- tion of ERK1/2 andcontributes to pulmo- nary vascular remodeling. Early growth response-1induces and en- hances vascular endothelial growth factor- A expressionin lung cancer cells . . .

Example 6 Cooperative Proteins for Diagnosing Pulmonary Nodules

To achieve unbiased discovery of cooperative proteins, selected reactionmonitoring (SRM) mass spectrometry (Addona, Abbatiello et al. 2009) wasutilized. SRM is a form of mass spectrometry that monitors predeterminedand highly specific mass products of particularly informative(proteotypic) peptides of selected proteins. These peptides arerecognized as specific transitions in mass spectra. SRM possesses thefollowing required features that other technologies, notablyantibody-based technologies, do not possess:

-   -   Highly multiplexed SRM assays can be rapidly and        cost-effectively developed for tens or hundreds of proteins.    -   The assays developed are for proteins of one's choice and are        not restricted to a catalogue of pre-existing assays.        Furthermore, the assays can be developed for specific regions of        a protein, such as the extracellular portion of a transmembrane        protein on the cell surface of a tumor cell, or for a specific        isoform.    -   SRM technology can be used from discovery to clinical testing.        Peptide ionization, the foundation of mass spectrometry, is        remarkably reproducible. Using a single technology platform        avoids the common problem of translating an assay from one        technology plat-form to another.        SRM has been used for clinical testing of small molecule        analytes for many years, and recently in the development of        biologically relevant assays [10].

Labeled and unlabeled SRM peptides are commercially available, togetherwith an open-source library and data repository of mass spectra fordesign and conduct of SRM analyses. Exceptional public resources existto accelerate assay development including the PeptideAtlas [11] and thePlasma Proteome Project [12, 13], the SRM Atlas and PASSEL, thePeptideAtlas SRM Experimental Library (www.systemsbiology.org/passel).

Two SRM strategies that enhance technical performance were introduced.First, large scale SRM assay development introduces the possibility ofmonitoring false signals. Using an extension of expression correlationtechniques [14], the rate of false signal monitoring was reduced tobelow 3%. This is comparable and complementary to the approach used bymProphet (Reiter, Rinner et al. 2011).

Second, a panel of endogenous proteins was used for normalization.However, whereas these proteins are typically selected as “housekeeping”proteins (Lange, Picotti et al. 2008), proteins that were strongnormalizers for the technology platform were identified. That is,proteins that monitored the effects of technical variation so that itcould be controlled effectively. This resulted, for example, in thereduction of technical variation due to sample depletion of highabundance proteins from 23.8% to 9.0%. The benefits of endogenous signalnormalization has been previously discussed (Price, Trent et al. 2007).

The final component of the strategy was to carefully design thediscovery and validation studies using emerging best practices.Specifically, the cases (malignant nodules) and controls (benignnodules) were pairwise matched on age, nodule size, gender andparticipating clinical site. This ensures that the candidate markersdiscovered are not markers of age or variations in sample collectionfrom site to site. The studies were well-powered, included multiplesites, a new site participated in the validation study, and importantly,were designed to address the intended use of the test. The carefulselection and matching of samples resulted in an exceptionally valuablefeature of the classifier. The classifier generates a score that isindependent of nodule size and smoking status. As these are currentlyused risk factors for clinical management of IPNs, the classifier is acomplementary molecular tool for use in the diagnosis of IPNs.

Selection of Biomarker Candidates for Assay Development

To identify lung cancer biomarkers in blood that originate from lungtumor cells, resected lung tumors and distal normal tissue of the samelobe were obtained. Plasma membranes were isolated from both endothelialand epithelial cells and analyzed by tandem mass spectrometry toidentify cell surface proteins over expressed on tumor cells. Similarly,Golgi apparatus were isolated to identify over-secreted proteins fromtumor cells. Proteins with evidence of being present in blood orsecreted were prioritized resulting in a set of 217 proteins. SeeExample 7: Materials and Methods for details.

To ensure other viable lung cancer biomarkers were not overlooked, aliterature search was performed and manually curated for lung cancermarkers. As above, proteins with evidence of being present in blood orsecreted were prioritized. This resulted in a set of 319 proteins. SeeExample 7: Materials and Methods for details.

The tissue (217) and literature (319) candidates overlapped by 148proteins resulting in a final candidate list of 388 protein candidates.See Example 7: Materials and Methods.

Development of SRM Assays

SRM assays for the 388 proteins were developed using standard syntheticpeptide techniques (See Example 7: Materials and Methods). Of the 388candidates, SRM assays were successfully developed for 371 candidates.The 371 SRM assays were applied to benign and lung cancer plasma samplesto evaluate detection rate in blood. 190 (51% success rate) of the SRMassays were detected. This success rate compares favorably to similarattempts to develop large scale SRM assays for detection of cancermarkers in plasma. Recently 182 SRM assays for general cancer markerswere developed from 1172 candidates (16% success rate) [15]. Despitefocusing only on lung cancer markers, the 3-fold increase in efficiencyis likely due to sourcing candidates from cancer tissues with priorevidence of presence in blood. Those proteins of the 371 that werepreviously detected by mass spectrometry in blood had a 64% success rateof detection in blood whereas those without had a 35% success rate. Ofthe 190 proteins detected in blood, 114 were derived from thetissue-sourced candidates and 167 derived from the literature-sourcedcandidates (91 protein overlap). See Example 7: Materials and Methodsand Table 6.

Typically, SRM assays are manually curated to ensure assays aremonitoring the intended peptide. However, this becomes unfeasible forlarge scale SRM assays such as this 371 protein assay. More recently,computational tools such as mProphet (Reiter, Rinner et al. 2011) enableautomated qualification of SRM assays. A complementary strategy tomProphet was introduced that does not require customization for eachdataset set. It utilizes correlation techniques (Kearney, Butler et al.2008) to confirm the identity of protein transitions with highconfidence. In FIG. 7 a histogram of the Pearson correlations betweenevery pair of transitions in the assay is presented. The correlationbetween a pair of transitions is obtained from their expression profilesover all 143 samples in the discovery study detailed below. As expected,transitions from the same peptide are highly correlated. Similarly,transitions from different peptide fragments of the same protein arealso highly correlated. In contrast, transitions from different proteinsare not highly correlated and enables a statistical analysis of thequality of a protein's SRM assay. For example, if the correlation oftransitions from two peptides from the same protein is above 0.5 thenthere is less than a 3% probability that the assay is false. See Example7: Materials and Methods.

Classifier Discovery

A summary of the 143 samples used for classifier discovery appears inTable 17. Samples were obtained from three sites to avoid overfitting toa single site. Participating sites were Laval (Institut Universitaire deCardiologie et de Pneumologie de Quebec), NYU (New York University) andUPenn (University of Pennsylvania). Samples were also selected to berepresentative of the intended use population in terms of nodule size(diameter), age and smoking status.

Benign and cancer samples were paired by matching on age, gender, siteand nodule size (benign and cancer samples were required to have anodule identified radiologically). The benign and cancer samples displaya bias in smoking (pack years), however, the majority of benign andcancer samples were current or past smokers. In comparing malignant andbenign samples, the intent was to find proteins that were markers oflung cancer; not markers of age, nodule size or differences in sitesample collection. Note that cancer samples were pathologicallyconfirmed and benign samples were either pathologically confirmed orradiologically confirmed (no tumor growth demonstrated over two years ofCT scan surveillance).

TABLE 17 Clinical data summaries and demographic analysis for discoveryand validation sets. Discovery Validation Cancer Benign P value CancerBenign P value Sample 72 71 52 52 (total) Sample Laval 14 14 1.00† 13 120.89† (Center) NYU 29 28 6 9 UPenn 29 29 14 13 Vanderbilt 0 0 19 18Sample Male 29 28 1.00† 25 27 0.85† (Gender) Female 43 43 27 25 SampleNever 5 19 0.006† 3 15 0.006† (Smoking Past 60 44 38 29 History) Current6 6 11 7 No data 1 2 0 1 Age Median 65 (59-72) 64 (52-71) 0.46‡ 63(60-73) 62 (56-67) 0.03‡ (quartile range) Nodule Median 13 (10-16) 13(10-18) 0.69‡ 16 (13-20) 15 (12-22) 0.68‡ Size (mm) (quartile range)Pack-year§ Median 37 (20-52) 20 (0-40)  0.001‡ 40 (19-50) 27 (0-50) 0.09‡ (quartile range) †Based on Fisher's exact test. ‡Based onMann-Whitney test. §No data (cancer, benign): Discovery (4, 6),Validation (2, 3)

The processing of samples was conducted in batches. Each batch containeda set of randomly selected cancer-benign pairs and three plasmastandards, included for calibration and quality control purposes.

All plasma samples were immunodepleted, trypsin digested and analyzed byreverse phase HPLC-SRM-MS. Protein transitions were normalized using anendogenous protein panel. The normalization procedure was designed toreduce overall variability, but in particular, the variabilityintroduced by the depletion step. Overall technical variability wasreduced from 32.3% to 25.1% and technical variability due to depletionwas reduced from 23.8% to 9.0%. De-tails of the sample analysis andnormalization procedure are available in Example 7: Materials andMethods.

To assess panels of proteins, they were fit to a logistic regressionmodel. Logistic regression was chosen to avoid the overfitting that canoccur with non-linear models, especially when the number of variablesmeasured (transitions) is similar or larger than the number of samplesin the study. The performance of a panel was measured by partial areaunder the curve (AUC) with sensitivity fixed at 90% (McClish 1989).Partial AUC correlates to high NPV performance while maximizing ROR.

To derive the 13 protein classifier, four criteria were used:

-   -   The protein must have transitions that are reliably detected        above noise across samples in the study.

The protein must be highly cooperative.

-   -   The protein must have transitions that are robust (high signal        to noise, no interference, etc.)    -   The protein's coefficient within the logistic regression model        must have low variability during cross validation, that is, it        must be stable.        Details of how each of these criteria were applied appear in        Example 7: Materials and Methods.

Finally, the 13 protein classifier was trained to a logistic regressionmodel by Monte Carlo cross validation (MCCV) with a hold out rate of 20%and 20,000 iterations. The thirteen proteins for the rule-out classifierare listed in Table 18 along with their highest intensity transition andmodel coefficient.

TABLE 18 The 13 protein classifier. Protein Transition CoefficientConstant(α) 36.16 LRP1_HUMAN TVLWPNGLSLDIPAGR_855.00_400.20 −1.59BGH3_HUMAN LTLLAPLNSVFK_658.40_804.50 1.73 COIA1_HUMANAVGLAGTFR_446.26_721.40 −1.56 TETN_HUMAN LDTLAQEVALLK_657.39_330.20−1.79 TSP1_HUMAN GFLLLASLR_495.31_559.40 0.53 ALDOA_HUMANALQASALK_401.25_617.40 −0.80 GRP78_HUMAN TWNDPSVQQDIK_715.85_260.20 1.41ISLR_HUMAN ALPGTPVASSQPR_640.85_841.50 1.40 FRIL_HUMANLGGPEAGLGEYLFER_804.40_913.40 0.39 LG3BP_HUMAN VEIFYR_413.73_598.30−0.58 PRDX1_HUMAN QITVNDLPVGR_606.30_428.30 −0.34 FIBA_HUMANNSLFEYQK_514.76_714.30 0.31 GSLG1_HUMAN IIIQESALDYR_660.86_338.20 −0.70

Validation of the Rule-Out Classifier

52 cancer and 52 benign samples (see Table 17) were used to validate theperformance of the 13 protein classifier. All samples were independentof the discovery samples, in addition, over 36% of the validationsamples were sourced from a new fourth site (Vanderbilt University).Samples were selected to be consistent with intended use and matched interms of gender, clinical site and nodule size. We note a slight agebias, which is due to 5 benign samples from young patients. Anticipatinga NPV of 90%, the 95% confidence interval is +/−5%.

At this point we refer to the 13 protein classifier trained on 143samples the Discovery classifier. However, once validation is completed,to find the optimal coefficients for the classifier, it was retrained onall 247 samples (discovery and validation sets) as this is mostpredictive of future performance. We refer to this classifier as theFinal classifier. The coefficients of the Final classifier appear inTable 21.

The performance of the Discovery and Final classifiers is summarized inFIG. 8. Reported are the NPV and ROR for the Discovery classifier whenapplied to the discovery set, the validation set. The NPV and ROR forthe Final classifier are reported for all samples and also for allsamples restricted to nodule size 8 mm to 20 mm (191 samples).

NPV and ROR are each reported as a fraction from 0 to 1. Similarly, theclassifier produces a score between 0 and 1, which is the probability ofcancer predicted by the classifier.

The discovery and validation curves for NPV and ROR are similar with thediscovery curves superior as expected. This demonstrates thereproducibility of performance on an independent set of samples. ADiscovery classifier rule out threshold of 0.40 achieves NPV of 96% and90%, whereas ROR is 33% and 23%, for the discovery samples and thevalidation samples, respectively. Final classifier rule threshold of0.60 achieves NPV of 91% and 90%, whereas ROR is 45% and 43%, for allsamples and all samples restricted to be 8 mm-20 mm, respectively.

Applications of the Classifier

FIG. 9 presents the application of the final classifier to all 247samples from the discovery and validation sets. The intent of FIG. 9 isto contrast the clinical risk factors of smoking (measured in packyears) and nodule size (proportional to the size of each circle) to theclassifier score assigned to each sample.

First, note the density of cancer samples with high classifier scores.The classifier has been designed to detect a cancer signature in bloodwith high sensitivity. As a consequence, to the left of the rule outthreshold (0.60) there are very few (<10%) cancer samples, assumingcancer prevalence of 25% [16, 17].

Third is the observation that nodule size does not appear to increasewith the classifier score. Both large and small nodules are spreadacross the classifier score spectrum. Similarly, although there are afew very heavy smokers with very high classifier scores, increasedsmoking does not seem to increase with classifier score. To quantifythis observation the correlation between the classifier score and nodulesize, smoking and age were calculated and appear in Table 19. In allcases there is no significant relationship between the classifier scoreand the risk factors. The one exception is a weak correlation betweenbenign classifier scores and benign ages. However, this correlation isso weak that the classifier score increases by only 0.04 every 10 years.

TABLE 19 Correlation between classifier scores and clinical riskfactors. Age Nodule Size Smoking Benign 0.25 −0.06 0.11 Cancer 0.01−0.01 0.06

This lack of correlation has clinical utility. It implies that theclassifier provides molecular information about the disease status of anIPN that is incremental upon risk factors such as nodule size andsmoking status. Consequently, it is a clinical tool for physicians tomake more informed decisions around the clinical management of an IPN.

To visual how this might be accomplished, we demonstrate how the cancerprobability score generated by the classifier can be related to cancerrisk (see FIG. 11)

At a given classifier score, some percentage of all cancer nodules willhave a smaller score. This is the sensitivity of the classifier. Forexample, at classifier score 0.8, 47% of cancer patients have a lowerscore, at classifier score 0.7, 28% of cancer patients have a lowerscore, at classifier score 0.5, only 9% are lower and finally at score0.25, only 4% are lower. This enables a physician to interpret apatient's classifier score in terms of relative risk.

The Molecular Foundations of the Classifier

The goal was to identify the molecular signature of a malignantpulmonary nodule by selecting proteins that were the cooperative,robustly detected by SRM and stable within the classifier. How wellassociated with lung cancer is the derived classifier? Is there amolecular foundation for the perturbation of these 13 proteins in blood?And finally, how unique is the classifier among other possible proteincombinations?

To answer these questions the 13 proteins of the classifier weresubmitted for pathway analysis using IPA (Ingenuity Systems,www.ingenuity.com). The first step was to work from outside the cellinwards to identify the transcription factors most likely to cause amodulation of these 13 proteins. The five most significant were FOS,NRF2, AHR, HD and MYC. FOS is common to many forms of cancer. However,NRF2 and AHR are associated with lung cancer, response to oxidativestress and lung inflammation. MYC is associated with lung cancer andresponse to oxidative stress while HD is associated with lunginflammation and response to oxidative stress.

The 13 classifier proteins are also highly specific to these threenetworks (lung cancer, response to oxidative stress and lunginflammation). This is summarized in FIG. 10 where the classifierproteins (green), transcription factors (blue) and the three mergednetworks (orange) are depicted. Only ISLR is not connected through thesethree lung specific networks to the other proteins, although it isconnected through cancer networks not specific to cancer. In summary,the modulation of the 13 classifier proteins can be tracked back to afew transcription factors specific to lung cancer, lung inflammation andoxidative stress networks.

To address the question of classifier uniqueness, every classifier fromthe 21 robust and cooperative proteins was formed (Table 20). Due to thecomputational overhead, these classifiers could not be fully trained byMonte Carlo cross validation, consequently, only estimates of theirperformance could be obtained. Five high preforming alternativeclassifiers were identified and then fully trained. The classifier andthe five high performing alternatives appear in Table 20. The frequencyof each protein appears in the tally column, in particular, the first 11proteins appear in 4 out of the 6 classifiers. These 11 proteins havesignificantly higher cooperative scores than the remaining proteins. Bythis analysis it appears that there is a core group of proteins thatform the blood signature of a malignant nodule.

TABLE 20 The classifier and the high performing alternatives;coefficients for proteins the respective panels are shown. Panel PanelPanel Panel Panel Protein Cooperative Protein Classifier 110424 130972126748 109919 60767 Tally Score Constant 36.16 27.72 27.69 23.47 21.3223.17 — — ALDOA −0.8 −0.67 −0.87 −0.83 −0.64 −0.68 6 1.3 COIA1 −1.56−1.04 −1.68 −1.37 −0.94 −1.2 6 3.7 TSP1 0.53 0.53 0.39 0.42 0.47 0.41 61.8 FRIL 0.39 0.45 0.39 0.41 0.41 0.41 6 2.8 LRP1 −1.59 −0.84 −1.32 1.15−0.84 −0.87 6 4.0 GRP78 1.41 1.14 1.31 −0.34 0.78 0.6 6 1.4 ISLR 1.41.03 1.08 0.75 0.74 5 1.4 IBP3 −0.23 −0.21 −0.38 −0.33 −0.54 5 3.4 TETN−1.79 −1.23 −1.99 −1.26 4 2.5 PRDX1 −0.34 −0.38 −0.36 −0.4 4 1.5 LG3BP−0.58 −0.61 −0.38 −0.48 4 4.3 CD14 0.99 1.08 1.4 3 4.0 BGH3 1.73 1.67−0.83 3 1.8 KIT −0.31 −0.56 3 1.4 GGH 0.44 0.52 3 1.3 AIFM1 −0.51 1 1.4FIBA 0.31 1 1.1 GSLG1 −0.7 1 1.2 ENPL 0 1.1 EF1A1 0 1.2 TENX 0 1.1

This result suggests that there is a core group of proteins that definea high performance classifier, but alternative panels exist. However,changes in panel membership affect the tradeoff between NPV and ROR.

Example 7 Materials and Methods

Assay Development Candidates Sourced from Tissue

Patient samples obtained from fresh lung tumor resections were collectedfrom Centre Hospitalier de l'Université de Montréal and McGillUniversity Health Centre under IRB approval and with informed patientconsent. Samples were obtained from the tumor as well as from distalnormal tissue in the same lung lobe. Plasma membranes of each pair ofsamples were then isolated from the epithelial cells of 30 patients (19adenocarcinoma, 6 squamous, 5 large cell carcinoma) and endothelialcells of 38 patients (13 adenocarcinoma, 18 squamous, 7 large cellcarcinoma) using immune-affinity protocols. Golgi apparatus wereisolated from each pair of samples from 33 patients (18 adenocarcinoma,14 squamous, 1 adenosquamous) using isopycnic centrifugation followed byammonium carbonate extraction. Plasma membrane isolations and Golgiisolations were then analyzed by tandem mass spectrometry to identifyproteins overexpressed in lung cancer tissue over normal tissue, forboth plasma membranes and Golgi.

Assay Development Candidates Sourced from Literature

Candidate lung cancer biomarkers were identified from two public and onecommercial database: Entrez (www.ncbi.nlm.nih.gov/books/NBK3836),UniProt (www.uniprot.org) and NextBio (www.nextbio.com). Terminologieswere predefined for the database queries which were automated using PERLscripts. The mining was carried out on May 6, 2010 (UniProt), May 17,2010 (Entrez) and Jul. 8, 2010 (NextBio), respectively. Biomarkers werethen assembled and mapped to UniProt identifiers.

Evidence of Presence in Blood

The tissue-sourced and literature-source biomarker candidates wererequired to have evidence of presence in blood. For evidence by massspectrometry detection, three datasets were used. HUPO9504 contains 9504human proteins identified by tandem mass spectrometry [13]. HUPO889, ahigher confidence subset of HUPO9504, contains 889 human proteins [18].The PeptideAtlas (November 2009 build) was also used. A biomarkercandidate was marked as previously detected if it contained at least oneHUPO889, or at least two HUPO9504 peptides, or at least two PeptideAtlaspeptides.

In addition to direct evidence of detection in blood by massspectrometry, annotation as secreted proteins or as single-pass membraneproteins [19] were also accepted as evidence of presence in blood.Furthermore, proteins in UniProt or designation as plasma proteins threeprograms for predicting whether or not a protein is secreted into theblood were used. These programs were TMHMM [20], SignalP [21] andSecretomeP [22]. A protein was predicted as secreted if TMHMM predictedthe protein had one transmembrane domain and SignalP predicted thetransmembrane domain was cleaved; or TMHMM predicted the protein had notransmembrane domain and either SignalP or SecretomeP predicted theprotein was secreted.

SRM Assay Development

SRM assays for 388 targeted proteins were developed based on syntheticpeptides, using a protocol similar to those described in the literature[15, 23, 24]. Up to five SRM suitable peptides per protein wereidentified from public sources such as the PeptideAtlas, Human PlasmaProteome Database or by proteotypic prediction tools [25] andsynthesized. SRM triggered MS/MS spectra were collected on an ABSciex5500 QTrap for both doubly and triply charged precursor ions. Theobtained MS/MS spectra were assigned to individual peptides using MASCOT(cutoff score ≧15) [26]. Up to four transitions per precursor ion wereselected for optimization. The resulting corresponding optimal retentiontime, declustering potential and collision energy were assembled for alltransitions. Optimal transitions were measured on a mixture of allsynthetic peptides, a pooled sample of benign patients and a pooledsample of cancer patients. Transitions were analyzed in batches, eachcontaining up to 1750 transitions. Both biological samples wereimmuno-depleted and digested by trypsin and were analyzed on an ABSciex5500 QTrap coupled with a reversed-phase (RP) high-performance liquidchromatography (HPLC) system. The obtained SRM data were manuallyreviewed to select the two best peptides per protein and the two besttransitions per peptide. Transitions having interference with othertransitions were not selected. Ratios between intensities of the twobest transitions of peptides in the synthetic peptide mixture were alsoused to assess the specificity of the transitions in the biologicalsamples. The intensity ratio was considered as an important metricdefining the SRM assays.

Processing of Plasma Samples

Plasma samples were sequentially depleted of high- and medium-abundanceproteins using immuno-depletion columns packed with the IgY14-Supermixresin from Sigma. The depleted plasma samples were then denatured,digested by trypsin and desalted. Peptide samples were separated using acapillary reversed-phase LC column (Thermo BioBasic 18 KAPPA; columndimensions: 320 μm×150 mm; particle size: 5 μm; pore size: 300 Å) and anano-HPLC system (nanoACQUITY, Waters Inc.). The mobile phases were (A)0.2% formic acid in water and (B) 0.2% formic acid in acetonitrile. Thesamples were injected (8 μl) and separated using a linear gradient (98%A to 70% A over 19 minutes, 5 μl/minute). Peptides were eluted directlyinto the electrospray source of the mass spectrometer (5500 QTrapLC/MS/MS, AB Sciex) operating in scheduled SRM positive-ion mode (Q1resolution: unit; Q3 resolution: unit; detection window: 180 seconds;cycle time: 1.5 seconds). Transition intensities were then integrated bysoftware MultiQuant (AB Sciex). An intensity threshold of 10,000 wasused to filter out noisy data and undetected transitions.

Plasma Samples Used for Discovery and Validation Studies

Aliquots of plasma samples were provided by the Institut Universitairede Cardiologie et de Pneumologie de Quebec (IUCPQ, Hospital Laval), NewYork University, the University of Pennsylvania, and VanderbiltUniversity (see Table 17). Subjects were enrolled in clinical studiespreviously approved by their Ethics Review Board (ERB) or InstitutionalReview Boards (IRB), respectively. In addition, plasma samples wereprovided by study investigators after review and approval of thesponsor's study protocol by the respective institution's IRB asrequired. Sample eligibility for the proteomic analysis was based on thesatisfaction of the study inclusion and exclusion criteria, includingthe subject's demographic information, the subject's corresponding lungnodule radiographic characterization by chest computed tomography (CT),and the histopathology of the lung nodule obtained at the time ofdiagnostic surgical resection. Cancer samples had a histopathologicdiagnosis of either non-small cell lung cancer (NSCLC), includingadenocarcinoma, squamous cell, large cell, or bronchoalveolar cellcarcinoma and a radiographic nodule of 30 mm or smaller. Benign samples,including granulomas, hamartomas and scar tissue, were also required tohave a radiographic nodule of 30 mm or smaller and eitherhistopathologic confirmation of being non-malignant or radiologicalconfirmation in alignment with clinical guidelines. To ensure theaccuracy of the clinical data, independent monitoring and verificationof the clinical data associated with both the subject and lung nodulewere performed in accordance with the guidance established by the HealthInsurance Portability and Accountability Act (HIPAA) of 1996 to ensuresubject privacy.

Study Design

The objective of the study design was to eliminate clinical andtechnical bias. Clinically, cancer and benign samples were paired sothat they were from the same site, same gender, nodule sizes within 10mm, age within 10 years, and smoking history within 20 pack years. Up to15 pairs of matched cancer and benign samples per batch were assignediteratively to processing batches until no statistical bias wasdemonstrable based on age, gender or nodule size.

Paired samples within each processing batch were further randomly andrepeatedly assigned to positions within the processing batch, until theabsolute values of the corresponding Pearson correlation coefficientsbetween position and gender, nodule size, and age were less than 0.1.Afterwards, each pair of cancer and benign samples was randomized totheir relative positions. To provide a control for sample batching,three 200 μl aliquots of a pooled human plasma standard (HPS)(Bioreclamation, Hicksville, N.Y.) were positioned at the beginning,middle and end of each processing batch, respectively. Samples within abatch were analyzed together.

Logistic Regression Model

The logistic regression classification method [27] was used to combine apanel of transitions into a classifier and to calculate a classificationprobability score between 0 and 1 for each sample. The probability score(P_(s)) of a sample was determined as P_(s)=1/[1+exp(−α−Σ_(i=1)^(N)β_(i)*{hacek over (I)}_(i,s))], where {hacek over (I)}_(i,s) was thelogarithmically transformed (base 2), normalized intensity of transitioni in sample s, β_(i) was the corresponding logistic regressioncoefficient, α was a classifier-specific constant, and N was the totalnumber of transitions in the classifier. A sample was classified asbenign if P_(s) was less than a decision threshold. The decisionthreshold can be increased or decreased depending on the desired NPV. Todefine the classifier, the panel of transitions (i.e. proteins), theircoefficients, the normalization transitions, classifier coefficient αand the decision threshold must be learned (i.e. trained) from thediscovery study and then confirmed using the validation study.

Discovery of the Rule-Out Classifier

A summary of the 143 samples used for classifier discovery appears inTable 17 and processed as described above.

Protein transitions were normalized as described above. Transitions thatwere not detected in at least 50% of the cancer samples or 50% of thebenign samples were eliminated leaving 117 transitions for furtherconsideration. Missing values for these transitions were replaced byhalf the minimum detected value over all samples for that transition.

The next step was finding the set of most cooperative proteins. Thecooperative score of a protein is the number of high performing panelsit participates in divided by the number of such panels it could appearon by chance alone. Hence, a cooperative score above 1 is good, and ascore below 1 is not. The cooperative score for each protein isestimated by the following procedure:

One million random panels of 10 proteins each, selected from the 117candidates, were generated. Each panel of 10 proteins was trained usingthe Monte Carlo cross validation (MCCV) method with a 20% hold-off rateand one hundred sample permutations per panel) to fit a logisticregression model and its performance assessed by partial AUC [28].

By generating such a large number of panels, we sample the space ofclassifiers sufficiently well to find some high performers by chance.The one hundred best random panels (see Table 2) out of the milliongenerated were kept and for each of the 117 proteins we determined howfrequently each occurred on these top panels. Of the 117 proteins, 36had frequency more than expected by chance, after endogenous normalizerswere removed. (Table 22) The expected number of panels on which aprotein would appear by chance is 100*10/117=8.33. The cooperative scorefor a protein is the number of panels it appears on divided by 8.33.

TABLE 21 Coefficient Coefficient Official (Discovery) (Final) PredictedProtein Gene Cooperative Coefficient alpha = alpha = TissueConcentration Category (UniProt) Name Score Partial AUC CV Transition36.16 26.25 Candidate (ng/ml) Classifier TSP1_HUMAN THBS1 1.8 0.25 0.24GFLLLASLR_495.31_559.40 0.53 0.44 510 Classifier COIA1_HUMAN COL18A1 3.70.16 0.25 AVGLAG- −1.56 −0.91 35 TFR_446.26_721.40 Classifier ISLR_HUMANISLR 1.4 0.32 0.25 ALPGTPVASS- 1.40 0.83 — QPR_640.85_841.50 ClassifierTETN_HUMAN CLEC3B 2.5 0.26 0.26 LDTLAQE- −1.79 −1.02 58000VALLK_657.39_330.20 Classifier FRIL_HUMAN FTL 2.8 0.31 0.26 LGG- 0.390.17 Secreted, Epi, Endo 12 PEAGLGEYLFER_804.40_913.40 ClassifierGRP78_HUMAN HSPA5 1.4 0.27 0.27 TWNDPSVQQDIK_715.85_260.20 1.41 0.55Secreted, Epi, Endo 100 Classifier ALDOA_HUMAN ALDOA 1.3 0.26 0.28ALQASALK_401.25_617.40 −0.80 −0.26 Secreted, Epi 250 ClassifierBGH3_HUMAN TGFBI 1.8 0.21 0.28 LTLLAPLNSVFK_658.40_804.50 1.73 0.54 Epi140 Classifier LG3BP_HUMAN LGALS3BP 4.3 0.29 0.29 VE- −0.58 −0.21Secreted 440 IFYR_413.73_598.30 Classifier LRP1_HUMAN LRP1 4.0 0.13 0.32TVLWPNGLSLDIPAGR_855.00_400.20 −1.59 −0.83 Epi 20 Classifier FI- FGA 1.10.31 0.35 NSLFEYQK_514.76_714.30 0.31 0.13 130000 BA_HUMAN ClassifierPRDX1_HUMAN PRDX1 1.5 0.32 0.37 QITVNDLPVGR_606.30_428.30 −0.34 −0.26Epi 60 Classifier GSLG1_HUMAN GLG1 1.2 0.34 0.45IIIQESALDYR_660.86_338.20 −0.70 −0.44 Epi, Endo — Robust KIT_HUMAN KIT1.4 0.33 0.46 8.2 Robust CD14_HUMAN CD14 4.0 0.33 0.48 Epi 420 RobustEF1A1_HUMAN EEF1A1 1.2 0.32 0.56 Secreted, Epi 61 Robust TENX_HUMAN TNXB1.1 0.30 0.56 Endo 70 Robust AIFM1_HUMAN AIFM1 1.4 0.32 0.70 Epi, Endo1.4 Robust GGH_HUMAN GGH 1.3 0.32 0.81 250 Robust IBP3_HUMAN IGFBP3 3.40.32 1.82 5700 Robust ENPL_HUMAN HSP90B1 1.1 0.29 5.90 Secreted, Epi,Endo 88 Non- ERO1A_HUMAN ERO1L 6.2 Secreted, Epi, Endo — Robust Non-6PGD_HUMAN PGD 4.3 Epi, Endo 29 Robust Non- ICAM1_HUMAN ICAM1 3.9 71Robust Non- PTPA_HUMAN PPP2R4 2.1 Endo 3.3 Robust Non- NCF4_HUMAN NCF42.0 Endo — Robust Non- SEM3G_HUMAN SEMA3G 1.9 — Robust Non- 1433T_HUMANYWHAQ 1.5 Epi 180 Robust Non- RAP2B_HUMAN RAP2B 1.5 Epi — Robust Non-MMP9_HUMAN MMP9 1.4 28 Robust Non- FOLH1_HUMAN FOLH1 1.3 — Robust Non-GSTP1_HUMAN GSTP1 1.3 Endo 32 Robust Non- EF2_HUMAN EEF2 1.3 Secreted,Epi 30 Robust Non- RAN_HUMAN RAN 1.2 Secreted, Epi 4.6 Robust Non-SODM_HUMAN SOD2 1.2 Secreted 7.1 Robust Non- DSG2_HUMAN DSG2 1.1 Endo2.7 RobustThe 36 most cooperative proteins are listed in Table 22.

TABLE 22 Coefficient Coefficient Official (Discovery) (Final) PredictedProtein Gene Cooperative Coefficient alpha = alpha = TissueConcentration Category (UniProt) Name Score Partial AUC CV Transition36.16 26.25 Candidate (ng/ml) Classifier TSP1_HUMAN THBS1 1.8 0.25 0.24GFLLLASLR_495.31_559.40 0.53 0.44 510 Classifier COIA1_HUMAN COL18A1 3.70.16 0.25 AVGLAGTFR_446.26_721.40 −1.56 −0.91 35 Classifier ISLR_HUMANISLR 1.4 0.32 0.25 ALPGTPVASS- 1.40 0.83 — QPR_640.85_841.50 ClassifierTETN_HUMAN CLEC3B 2.5 0.26 0.26 LDTLAQE- −1.79 −1.02 58000VALLK_657.39_330.20 Classifier FRIL_HUMAN FTL 2.8 0.31 0.26 LGG- 0.390.17 Secreted, 12 PEAGLGEYLFER_804.40_913.40 Classifier GRP78_HUMANHSPA5 1.4 0.27 0.27 TWNDPSVQQDIK_715.85_260.20 1.41 0.55 Secreted, Epi,Endo 100 Classifier ALDOA_HUMAN ALDOA 1.3 0.26 0.28ALQASALK_401.25_617.40 −0.80 −0.26 Secreted, Epi 250 ClassifierBGH3_HUMAN TGFBI 1.8 0.21 0.28 LTLLAPLNSVFK_658.40_804.50 1.73 0.54 Epi140 Classifier LG3BP_HUMAN LGALS3BP 4.3 0.29 0.29 VEIFYR_413.73_598.30−0.58 −0.21 Secreted 440 Classifier LRP1_HUMAN LRP1 4.0 0.13 0.32TVLWPNGLSLDIPAGR_855.00_400.20 −1.59 −0.83 Epi 20 Classifier FIBA_HUMANFGA 1.1 0.31 0.35 NSLFEYQK_514.76_714.30 0.31 0.13 130000 ClassifierPRDX1_HUMAN PRDX1 1.5 0.32 0.37 QITVNDLPVGR_606.30_428.30 −0.34 −0.26Epi 60 Classifier GSLG1_HUMAN GLG1 1.2 0.34 0.45IIIQESALDYR_660.86_338.20 −0.70 −0.44 Epi, Endo — Robust KIT_HUMAN KIT1.4 0.33 0.46 8.2 Robust CD14_HUMAN CD14 4.0 0.33 0.48 Epi 420 RobustEF1A1_HUMAN EEF1A1 1.2 0.32 0.56 Secreted, 61 Epi Robust TENX_HUMAN TNXB1.1 0.30 0.56 Endo 70 Robust AIFM1_HUMAN AIFM1 1.4 0.32 0.70 Epi, Endo1.4 Robust GGH_HUMAN GGH 1.3 0.32 0.81 250 Robust IBP3_HUMAN IGFBP3 3.40.32 1.82 5700 Robust ENPL_HUMAN HSP90B1 1.1 0.29 5.90 Secreted, Epi,Endo 88 Non-Robust ERO1A_HUMAN ERO1L 6.2 Secreted, Epi, Endo —Non-Robust 6PGD_HUMAN PGD 4.3 Epi, Endo 29 Non-Robust ICAM1_HUMAN ICAM13.9 71 Non-Robust PTPA_HUMAN PPP2R4 2.1 Endo 3.3 Non-Robust NCF4_HUMANNCF4 2.0 Endo — Non-Robust SEM3G_HUMAN SEMA3G 1.9 — Non-Robust1433T_HUMAN YWHAQ 1.5 Epi 180 Non-Robust RAP2B_HUMAN RAP2B 1.5 Epi —Non-Robust MMP9_HUMAN MMP9 1.4 28 Non-Robust FOLH1_HUMAN FOLH1 1.3 —Non-Robust GSTP1_HUMAN GSTP1 1.3 Endo 32 Non-Robust EF2_HUMAN EEF2 1.3Secreted, 30 Epi Non-Robust RAN_HUMAN RAN 1.2 Secreted, 4.6 EpiNon-Robust SODM_HUMAN SOD2 1.2 Secreted 7.1 Non-Robust DSG2_HUMAN DSG21.1 Endo 2.7

The set of 36 cooperative proteins was further reduced to a set of 21proteins by manually reviewing raw SRM data and eliminating proteinsthat did not have robust SRM transitions due to low signal to noise orinterference.

Proteins were iteratively eliminated from the set of 21 proteins until aclassifier with the optimal partial AUC was obtained. The criteria forelimination was coefficient stability. In a logistic regression modeleach protein has a coefficient. In the process of training the model thecoefficient for each protein is determined. When this is performed usingcross validation (MCCV), hundreds of coefficient estimates for eachprotein are derived. The variability of these coefficients is anestimate of the stability of the protein. At each step the proteins weretrained using MCCV (hold out rate 20%, ten thousand sample permutationsper panel) to a logistic regression model and their stability measured.The least stable protein was eliminated. This process continued until a13 protein classifier with optimal partial AUC was reached.

Finally, the 13 protein classifier was trained to a logistic regressionmodel by MCCV (hold out rate 20%, twenty thousand sample permutations).The thirteen proteins for the rule-out classifier are listed in Table 18along with their highest intensity transition and model coefficient.

Selection of a Decision Threshold

Assuming the cancer prevalence of lung nodules is prev, the performanceof a classifier (NPV and ROR) on the patient population with lungnodules was calculated from sensitivity (sens) and specificity (spec) asfollows:

$\begin{matrix}{{{NPV} = \frac{\left( {1 - {prev}} \right)*{spec}}{{{prev}*\left( {1 - {sens}} \right)} + {\left( {1 - {prev}} \right)*{spec}}}},} & (1) \\{{{PPV} = \frac{{prev}*{sens}}{{{prev}*{sens}} + {\left( {1 - {prev}} \right)*\left( {1 - {spec}} \right)}}},} & (2) \\{{ROR} = {{{prev}*\left( {1 - {sens}} \right)} + {\left( {1 - {prev}} \right)*{{spec}.}}}} & (3)\end{matrix}$

The threshold separating calls for cancer or benign samples was thenselected as the probability score with NPV ≧90% and ROR ≧20%. As weexpect the classifier's performance measured on the discovery set to bean overestimate, the threshold is selected to be a range, as performancewill usually degrade on an independent validation set.

Validation of the Rule-Out Classifier

52 cancer and 52 benign samples (see Table 17) were used to validate theperformance of the 13 protein classifier. Half of the samples wereplaced in pre-determined processing batches analyzed immediately afterthe discovery samples and the other half of samples were analyzed at alater date. This introduced variability one would expect in practice.More specifically, the three HPS samples run in each processing batchwere utilized as external calibrators. Details on HPS calibration aredescribed below.

Calibration by HPS Samples

For label-free MS approach, variation on signal intensity betweendifferent experiments is expected. To reduce this variation, we utilizedHPS samples as an external standard and calibrated the intensity betweenthe discovery and validation studies. Assume that {hacek over (I)}_(i,s)is the logarithmically transformed (base 2), normalized intensity oftransition i in sample s, {hacek over (I)}_(i,dis) and {hacek over(I)}_(i,val) are the corresponding median values of HPS samples in thediscovery and the validation studies, respectively. Then the HPScorrected intensity is

Ĩ _(i,s) ={hacek over (I)} _(i,s) −{hacek over (I)} _(i,val) +{hacekover (I)} _(i,dis)

Consequently, assume that the probability for cancer of a clinicalsample in the validation study is predicted as prob by the classifier.Then the HPS corrected probability of cancer of the clinical sample iscalculated as follows:

${probability}_{corrected} = \frac{1}{1 + ^{- S_{corrected}}}$ whereS_(corrected) = S − S_(HPS, val) + S_(HPS, dis) and$S = {\ln {\frac{prob}{1 - {prob}}.}}$

Here S_(HPS,dis) and S_(HPS,val) were the median value of S of all HPSsamples in the discovery and validation studies, respectively.

Statistical Analysis

All statistical analyses were performed with Stata, R and/or MatLab.

Depletion Column Drift

We observed an increase of signal intensity as more and more sampleswere depleted by the same column. We used transition intensity in HPSsamples to quantify this technical variability. Assuming I_(i,s) was theintensity of transition i in a HPS sample s, the drift of the sample wasdefined as

${{drift}_{s} = {{median}\left( \frac{I_{i,s} - {\hat{I}}_{s}}{{\hat{I}}_{s}} \right)}},$

where {hacek over (I)}_(i) was the mean value of I_(i,s) among all HPSsamples that were depleted by the same column and the median was takenover all detected transitions in the sample. Then the drift of thecolumn was defined as

drift_(col)=median(drift_(s)>0)−median(drift_(s)<0).

Here the median was taken over all HPS samples depleted by the column.If no sample drift was greater or less than zero, the correspondingmedian was taken as 0. The median column drift was the median of driftsof all depletion columns used in the study.

Identification of Endogenous Normalizing Proteins

The following criteria were used to identify a transition as anormalizer:

-   -   Possessed the highest median intensity of all transitions from        the same protein.    -   Detected in all samples.    -   Ranked high in reducing median technical CV (median CV of        transition intensities that were measured on HPS samples) as a        normalizer.    -   Ranked high in reducing median column drift that was observed in        sample depletion.    -   Possessed low median technical CV and low median biological CV        (median CV of transition intensities that were measured on        clinical samples).        Six transitions were selected and appear in Table 23.

TABLE 23 Panel of endogenous normalizers. Median Median Technical ColumnNormalizer Transition CV (%) Drift (%) PEDF_HUMANLQSLFDSPDFSK_692.34_593.30 25.8 6.8 MASP1_HUMANTGVITSPDFPNPYPK_816.92_258.10 26.5 18.3 GELS_HUMANTASDFITK_441.73_710.40 27.1 16.8 LUM_HUMAN SLEDLQLTHNK_433.23_499.3027.1 16.1 C163A_HUMAN INPASLDK_429.24_630.30 26.6 14.6 PTPRJ_HUMANVITEPIPVSDLR_669.89_896.50 27.2 18.2 Normalization by Panel ofTransitions 25.1 9.0 Without Normalization 32.3 23.8

Data Normalization

A panel of six normalization transitions (see Table 23) were used tonormalize raw SRM data for two purposes: (A) to reduce sample-to-sampleintensity variations within same study and (B) to reduce intensityvariations between different studies. For the first purpose, a scalingfactor was calculated for each sample so that the intensities of the sixnormalization transitions of the sample were aligned with thecorresponding median intensities of all HGS samples. Assuming thatN_(i,s) is the intensity of a normalization transition i in sample s and{circumflex over (N)}_(i) the corresponding median intensity of all HGSsamples, then the scaling factor for sample s is given by Ŝ/S_(s), where

$S_{s} = {{median}\left( {\frac{N_{1,s}}{{\hat{N}}_{1}},\frac{N_{2,s}}{{\hat{N}}_{2}},\ldots \mspace{14mu},\frac{N_{6,s}}{{\hat{N}}_{6}}} \right)}$

is the median of the intensity ratios and Ŝ is the median of S_(s) overall samples in the study. For the second purpose, a scaling factor wascalculated between the discovery and the validation studies so that themedian intensities of the six normalization transitions of all HGSsamples in the validation study were comparable with the correspondingvalues in the discovery study. Assuming that the median intensities ofall HGS samples in the two studies are {circumflex over (N)}_(i,dis) and{circumflex over (N)}_(i,val), respectively, the scaling factor for thevalidation study is given by

$R = {{median}\left( {\frac{{\hat{N}}_{1,{dis}}}{{\hat{N}}_{1,{val}}},\frac{{\hat{N}}_{2,{dis}}}{{\hat{N}}_{2,{val}}},\ldots \mspace{14mu},\frac{{\hat{N}}_{6,{dis}}}{{\hat{N}}_{6,{val}}}} \right)}$

Finally, for each transition of each sample, its normalized intensitywas calculated as

Ĩ _(i,s) =I _(i,s) *R*Ŝ/S _(s)

where I_(i,s) was the raw intensity.

Isolation of Membrane Proteins from Tissues

Endothelial plasma membrane proteins were isolated from normal and tumorlung tissue samples that were obtained from fresh lung resections.Briefly, tissues were washed in buffer and homogenates were prepared bydisrupting the tissues with a Polytron. Homogenates were filteredthrough a 180-μm mesh and filtrates were centrifuged at 900×g for 10min, at 4° C. Supernatants were centrifuged on top of a 50% (w:v)sucrose cushion at 218,000×g for 60 min at 4° C. to pellet themembranes. Pellets were resuspended and treated with micrococcalnuclease. Membranes from endothelial cells were incubated with acombination of anti-thrombomodulin, anti-ACE, anti-CD34 and anti-CD144antibodies, and then centrifuged on top of a 50% (w:v) sucrose cushionat 280,000×g for 60 min at 4° C. After pellets were resuspended,endothelial cell plasma membranes were isolated using MACS microbeads,treated with potassium iodide to remove cytoplasmic peripheral proteins.

Epithelial plasma membrane proteins from normal and tumor lung tissuesamples were isolated from fresh lung resections. Tissues were washedand homogenates as described above for endothelial plasma membraneproteins preparation. Membranes from epithelial cells were labeled witha combination of anti-ESA, anti-CEA, anti-CD66c and anti-EMA antibodies,and then centrifuged on top of a 50% (w:v) sucrose cushion at 218,000×gfor 60 min at 4° C. Epithelial cell plasma membranes were isolated usingMACS microbeads and the eluate was centrifuged at 337,000×g for 30minutes at 4° C. over a 33% (w:v) sucrose cushion. After removing thesupernatant and sucrose cushion, the pellet was resuspended inLaemmli/Urea/DTT.

Isolation of Secreted Proteins from Tissues

Secreted proteins were isolated from normal and tumor lung tissuesamples that were isolated from fresh lung resections. Tissues werewashed and homogenized using a Polytron homogenization. The density ofthe homogenates was adjusted to 1.4 M with concentrated sucrose prior toisolating the secretory vesicles by isopycnic centrifugation at100,000×g for 2 hr at 4° C. on a 0.8 and 1.2 M discontinuous sucrosegradient. Vesicles concentrating at the 0.8/1.2 M interface werecollected and further incubated for 25 minutes with 0.5 M KCl (finalconcentration) to remove loosely bound peripheral proteins. Vesicleswere recuperated by ultracentrifugation at 150,000×g for one hour at 4°C. and then opened with 100 mM ammonium carbonate pH 11.0 for 30 minutesat 4° C. Secreted proteins were recovered in the supernatant following a1-hour ultracentrifugation at 150,000×g at 4° C.

Preparation of IgY14-SuperMix Immunoaffinity Columns

Immunoaffinity columns were prepared in-house using a slurry containinga 2:1 ratio of IgY14 and SuperMix immunoaffinity resins, respectively(Sigma Aldrich). Briefly, a slurry (10 ml, 50%) of mixed immunoaffinityresins was added to a glass chromatography column (Tricorn, GEHealthcare) and the resin was allowed to settle under gravity flow,resulting in a 5 ml resin volume in the column. The column was cappedand placed on an Agilent 1100 series HPLC system for further packing (20minutes, 0.15M ammonium bicarbonate, 2 ml/min). The performance of eachcolumn used in the study was then assessed by replicate injections ofaliquots of HPS sample. Column performance was assessed prior tobeginning immunoaffinity separation of each batch of clinical samples.

IgY14-Sumermix Immunoaffinity Chromatography

Plasma samples (60 μl) were diluted (0.15M ammonium bicarbonate, 1:2v/v, respectively) and filtered (0.2 μm AcroPrep 96-well filter plate,Pall Life Sciences) prior to immunoaffinity separation. Dilute plasma(90 μl) was separated on the IgY14-SuperMix column connected to anAgilent 1100 series HPLC system using a three buffers (loading/washing:0.15M ammonium bicarbonate; stripping/elution: 0.1M glycine, pH 2.5;neutralization: 0.01M Tris-HCl, 0.15M NaCl, pH 7.4) with aload-wash-elute-neutralization-re-equilibration cycle (36 minutes totaltime). The unbound and bound fractions were monitored using a UVabsorbance (280 nm) and were baseline resolved after separation. Onlythe unbound fraction containing the low abundance proteins was collectedfor downstream processing and analysis. Unbound fractions werelyophilized prior to enzymatic digestion.

Enzymatic Digestion of Low Abundance Proteins

Low abundance proteins were reconstituted under mild denaturingconditions (200 μl of 1:1 0.1M ammonium bicarbonate/trifluoroethanolv/v) and allowed to incubate (30 minutes, room temperature, orbitalshaker). Samples were then diluted (800 μl of 0.1M ammonium bicarbonate)and digested with trypsin (Princeton Separations; 0.4 μg trypsin persample, 37° C., 16 hours). Digested samples were lyophilized prior tosolid-phase extraction.

Solid-Phase Extraction

Solid phase extraction was used to reduce salt and buffer contents inthe samples prior to mass spectrometry. The lyophilized samplescontaining tryptic peptides were reconstituted (350 μl 0.01M ammoniumbicarbonate) and allowed to incubate (15 minutes, room temperature,orbital shaker). A reducing agent was then added to the samples (30 μl0.05M TCEP) and the samples were incubated (60 minutes, roomtemperature). Dilute acid and a low percentage of organic solvent (375μl 90% water/10% acetonitrile/0.2% trifluoroacetic acid) were added tooptimize the solid phase extraction of peptides. The extraction plate(Empore C18, 3M Bioanalytical Technologies) was conditioned according tomanufacturer protocol. Samples were loaded onto the solid phaseextraction plate, washed (500 μl 95% water/5% acetonitrile/0.1%trifluoroacetic acid) and eluted (200 μl 52% water/48% acetonitrile/0.1%trifluoroacetic acid) into a collection plate. The eluate was split intotwo equal aliquots and each aliquot was taken to dryness in a vacuumconcentrator. One aliquot was used immediately for mass spectrometry,while the other was stored (−80° C.) and used as needed. Samples werereconstituted (12 μl 90% water/10% acetonitrile/0.2% formic acid) justprior to LC-SRM MS analysis.

Inclusion and Exclusion Criteria

Plasma samples were eligible for the studies if they were (A) obtainedin EDTA tubes, (B) obtained from subjects previously enrolled inIRB-approved studies at the participating institutions, and (C)archived, e.g. labeled, aliquotted and frozen, as stipulated by thestudy protocols. The samples must also satisfy the following inclusionand exclusion criteria:

-   -   1) Inclusion Criteria:    -   2) Sample eligibility was based on clinical parameters,        including the following subject, nodule and clinical staging        parameters:        -   a) Subject            -   i) age ≧40            -   ii) any smoking status, e.g. current, former, or never            -   iii) co-morbid conditions, e.g. COPD            -   iv) prior malignancy with a minimum of 5 years in                clinical remission            -   v) prior history of skin carcinomas—squamous or basal                cell        -   b) Nodule            -   i) Radiology                -   (1) size ≧4 mm and ≦70 mm (up to Stage 2B eligible)                -   (2) any spiculation or ground glass opacity            -   ii) pathology                -   (1) malignant—adenocarcinoma, squamous, or large                    cell                -   (2) benign—inflammatory (e.g. granulomatous,                    infectious) or non-inflammatory (e.g. hamartoma)        -   c) Clinical stage            -   i) Primary tumor: ≦T2 (e.g. 1A, 1B, 2A and 2B)            -   ii) Regional lymph nodes: N0 or N1 only            -   iii) Distant metastasis: M0 only    -   3) Exclusion Criteria        -   a) Subject: prior malignancy within 5 years of IPN diagnosis        -   b) Nodule:            -   i) size data unavailable            -   ii) for cancer or benign SPNs, no pathology data                available            -   iii) pathology—small cell lung cancer        -   c) Clinical stage            -   i) Primary tumor: ≧T3            -   ii) Regional lymph nodes: ≧N2            -   iii) Distant metastasis: ≧M1

Power Analysis for the Discovery Study

The power analysis for the discovery study was based on the followingassumptions: 1) The overall false positive rate (α) was set to 0.05. 2){hacek over (S)}idâk correction for multiple testing was used tocalculate the effective α_(eff) for testing 200 proteins,

${i.e.},\mspace{14mu} {\alpha_{eff} = {1 - {\sqrt[200]{1 - \alpha}.}}}$

3) The effective sample size was reduced by a factor of 0.864 to accountfor the larger sample requirement for the Mann-Whitney test than for thet-test. 4) The overall coefficient of variation was set to 0.43 based ona previous experience. 5) The power (1-β) of the study was calculatedbased on the formula for the two-sample, two-sided t-test, usingeffective α_(eff) and effective sample size. The power for the discoverystudy was tabulated in Table 24 by the sample size per cohort and thedetectable fold difference between control and disease samples.

TABLE 24 Cohort size required to detect protein fold changes with agiven probability. Detectable Protein Fold Difference Cohort Size 1.251.5 1.75 2 20 0.011 0.112 0.368 0.653 30 0.025 0.277 0.698 0.925 400.051 0.495 0.905 0.992 50 0.088 0.687 0.977 0.999 60 0.129 0.812 0.9941 70 0.183 0.902 0.999 1 80 0.244 0.953 1 1 90 0.302 0.977 1 1 100 0.3690.99 1 1

Power Analysis for the Validation Study

Sufficient cancer and benign samples are needed in the validation studyto confirm the performance of the rule-out classifier obtained from thediscovery study. We are interested in obtaining the 95% confidenceintervals (CIs) on NPV and ROR for the rule-out classifier. Using theEquations in the Selection of a Decision Threshold section herein, onecan derive sensitivity (sens) and specificity (spec) as functions of NPVand ROR, i.e.,

sens=1−ROR*(1−NPV)/prev,

spec=ROR*NPV1(1−prev),

where prev is the cancer prevalence in the intended use population.Assume that the validation study contains N_(C) cancer samples and N_(B)benign samples. Based on binomial distribution, variances of sensitivityand specificity are given by

var(sens)=sens*(1−sens)/N _(C)

var(spec)=spec*(1−spec)/N _(B)

Using the Equations in the Selection of a Decision Threshold sectionherein, the corresponding variances of NPV and ROR can be derived underthe large-sample, normal-distribution approximation as

${{{var}({NPV})} = {{{NPV}^{2}\left( {1 - {NPV}} \right)}^{2}\left\lbrack {\frac{{var}({sens})}{\left( {1 - {sens}} \right)^{2}} + \frac{{var}({spec})}{{spec}^{2}}} \right\rbrack}},{{{var}({ROR})} = {{{prev}^{2}*{{var}({sens})}} + {\left( {1 - {prev}} \right)^{2}*{{{var}({spec})}.}}}}$

The two-sided 95% CIs of NPV and ROR are then given by ±z_(α/2)√{squareroot over (var(NPV))} and ±z_(α/2)√{square root over (var(ROR))},respectively, where z_(α/2)=1.959964 is the 97.5% quantile of the normaldistribution. The anticipated 95% CIs for the validation study weretabulated in Table 25 by the sample size (N_(C)=N_(B)=N) per cohort.

TABLE 25 The 95% confidence interval (CI) of NPV as a function of cohortsize. The corresponding 95% CI of ROR is also listed. The prevalence wasset at 28.5%. The expected NPV and ROR were set to values in thediscovery study, i.e., 90% and 52%, respectively. 95% CI of 95% CI ofCohort Size NPV (± %) ROR (± %) 10 12.5 22.1 20 8.8 15.7 30 7.2 12.8 406.2 11.1 50 5.6 9.9 60 5.1 9.0 70 4.7 8.4 80 4.4 7.8 90 4.2 7.4 100 3.97.0 150 3.2 5.7 200 2.8 5.0

Calculation of Q-Values of Peptide and Protein Assays

To determine the false positive assay rate the q-values of peptide SRMassays were calculated as follows. Using the distribution of Pearsoncorrelations between transitions from different proteins as the nulldistribution (FIG. 7), an empirical p-value was assigned to a pair oftransitions from the same peptide, detected in at least five commonsamples otherwise a value of ‘NA’ is assigned. The empirical p-value wasconverted to a q-value using the “qvalue” package in Bioconductor(www.bioconductor.org/packages/release/bioc/html/qvalue.html). Peptideq-values were below 0.05 for all SRM assays presented in Table 6.

The q-values of protein SRM assays were calculated in the same wayexcept Pearson correlations of individual proteins were calculated asthose between two transitions from different peptides of the protein.For proteins not having two peptides detected in five or more commonsamples, their q-values could not be properly evaluated and wereassigned ‘NA’.

Impact of Categorical Confounding Factors

TABLE 26 Impact of categorical confounding factors on classifier score.Cancer p-value Benign p-value Gender # Female 70 0.786* 68 0.387* Medianscore 0.701 0.570 (quartile range) (0.642-0.788) (0.390-0.70)  # Male 5455 Median 0.736 0.621 (quartile range) (0.628-0.802) (0.459-0.723)Smoking # Never 8 0.435** 34 0.365** Status Median score 0.664 0.554(quartile range) (0.648-0.707) (0.452-0.687) # Past 98 73 Median 0.7030.586 (quartile range) (0.618-0.802) (0.428-0.716) # Current 17 13Median score 0.749 0.638 (quartile range) (0.657-0.789) (0.619-0.728)*p-value by Mann-Whitney test **p-value by Kruskal-Wallis test

Impact of Continuous Confounding Factors

TABLE 27 Impact of continuous confounding factors on classifier score.Coefficient of Correlation linear fit (95% CI) p-value Age All 0.1980.003 0.002  (0.001-0.005) Cancer 0.012 0.000 0.893 (−0.003-0.003)Benign 0.248 0.004 0.006  (0.001-0.007) Nodule All −0.057 −0.002 0.372size (−0.005-0.002) Cancer −0.013 0.000 0.889 (−0.005-0.004) Benign−0.055 −0.001 0.542 (−0.006-0.003) Pack- All 0.154 0.001 0.019 year (0.00-0.002) Cancer 0.060 0.000 0.520 (−0.001-0.001) Benign 0.108 0.0010.254  (0.00-0.002)

Example 8 A Systems Biology-Derived, Blood-Based Proteomic Classifierfor the Molecular Characterization of Pulmonary Nodules

Summary

Each year millions of pulmonary nodules are discovered by computedtomography but remain undiagnosed as malignant or benign. As themajority of these nodules are benign, many patients undergo unnecessaryand costly invasive procedures. This invention presents a 13-proteinblood-based classifier for the identification of benign nodules. Using asystems biology strategy, 371 protein candidates were identified andselected reaction monitoring (SRM) assays developed for each. The SRMassays were applied in a multisite discovery study (n=143) with benignand cancer plasma samples matched on nodule size, age, gender andclinical site. Rather than identify the best individual performingproteins, the 13-protein classifier was formed from proteins performingbest on panels. The classifier was validated on an independent set ofplasma samples (n=104) demonstrating high negative predictive value(92%) and specificity (27%) sufficiently high to obviate one-in-fourpatients with benign nodules from invasive procedures. Importantly,validation performance on a non-discovery clinical site showed NPV of100% and specificity of 28%, arguing for the general effectiveness ofthe classifier. A pathway analysis demonstrated that the classifierproteins are likely modulated by a few transcription regulators (NF2L2,AHR, MYC, FOS) highly associated with lung cancer, lung inflammation andoxidative stress networks. Remarkably, the classifier score wasindependent of patient nodule size, smoking history and age. As theseare the currently used risk factors for clinical management of pulmonarynodules, the application of this molecular test would provide a powerfulcomplementary tool for physicians to use in lung cancer diagnosis.

Rationale

Computed tomography (CT) identifies millions of pulmonary nodulesannually with many being undiagnosed as malignant or benign. The vastmajority of these nodules are benign, but due to the threat of cancer, asignificant number of patients with benign nodules undergo unnecessaryinvasive medical procedures costing the healthcare system billions ofdollars annually. Consequently, there is a high unmet need for anon-invasive clinical test that can identify benign nodules with highprobability.

Presented is a 13-protein plasma test, or classifier, for identifyingbenign nodules. To develop the classifier, a systems biology approachbased on the supposition that biological networks in tumors becomedisease-perturbed and alter the expression of their cognate proteins wasadopted. This systems approach employs a variety of strategies toidentify blood proteins that directly reflect lung cancer-perturbednetworks.

First, candidate biomarkers prioritized for inclusion on the classifierwere those proteins secreted by or shed from the cell surface of lungcancer cells in contrast to normal lung cells. These are proteins bothassociated with lung cancer and also most likely to be emitted by amalignant pulmonary nodule into blood. The literature was also surveyedto identify blood proteins associated with lung cancer. In total, aninitial list of 388 protein candidates for inclusion on the classifierwere derived from these three sources.

Another system-driven approach was to prioritize the 388 proteincandidates for inclusion on the classifier by how frequently they appearon high performing protein panels, as opposed to their individualdiagnostic performance. This strategy is motivated by the intent tocapture the integrated behavior of proteins within lung cancer-perturbednetworks. Proteins that appear frequently on high performing panels arecalled cooperative proteins. This is a defining step in the discovery ofthe classifier as the most cooperative proteins are often not theproteins with best individual performance.

Third, the classifier is deconstructed in terms of its relationship tolung cancer networks. Ideally, the classifier consists of multipleproteins from multiple lung cancer-perturbed networks. We conjecturethat measuring multiple proteins from the same lung cancer associatedpathway increases the signal-to-noise ratio thus enhancing performanceof the classifier.

Selected reaction monitoring (SRM) mass spectrometry (MS) was utilizedto measure the concentrations of the candidate proteins in plasma. SRMis a form of MS that monitors predetermined and highly specific massproducts, called transitions, of particularly informative (proteotypicor protein-specific) peptides of targeted proteins. Briefly, SRM assaysfor proteins are based on the high reproducibility of peptideionization, the foundation of MS. During a SRM analysis, the massspectrometer is programmed to monitor for transitions of the specificprotein(s) being assayed. The resulting chromatograms are integrated toprovide quantitative or semi-quantitative protein abundance information.The benefits of SRM assays include high protein specificity, largemultiplexing capacity, and both rapid and reliable assay development anddeployment. SRM has been used for clinical testing of small moleculeanalytes for many years, and recently in the development of biologicallyrelevant assays. Exceptional public resources exist to accelerate SRMassay development including the PeptideAtlas, the Plasma ProteomeProject, the SRM Atlas and the PeptideAtlas SRM Experimental Library(www.systemsbiology.org/passel).

In accordance with evolving guidelines for clinical test development,the classifier was discovered (n=143) and validated (n=104) usingindependent plasma sets from multiple clinical sites consistent with anintended use population of patients with lung nodules, defined as roundopacities up to 30 mm in size. In contrast to other biomarker studies,utilizing biospecimens associated with the broad clinical spectrum oflung cancer (Stages I to IV), the cancer plasma samples analyzed werelimited to Stage IA, which corresponds to the intended use population oflung nodules of size 30 mm or less. The classifier yielded a performanceamendable to further clinical stratification of the intended use byparameters such as age, smoking history or nodule size, as guided by aclinician's diagnostic needs.

Validated performance of the 13-protein classifier demonstrated anegative predictive value (NPV) of 92% and a specificity of 27%. Forclinical utility, the classifier must reliably and frequently provideinformation that can participate in a physician's decision to avoid aninvasive procedure. High NPV is required to ensure that the classifierreliably identifies benign nodules. Equivalently, malignant nodules arerarely (8% or less) reported as benign by the classifier. A specificityof 27% implies that one-in-four patients with a benign nodule can avoidinvasive procedures, and so, frequently provides information of clinicalutility. All validation samples were independent of discovery samples,and 37 came from a new clinical site. Performance on the samples fromthe new site demonstrated a NPV of 100% and a specificity of 28%suggesting that the classifier performance extends to new clinicalsettings. Remarkably, the classifier score is demonstrated to beindependent of the patient's age, smoking history and nodule size,thereby complementing current clinical risk factors with an informativemolecular dimension for evaluating the disease status of a pulmonarynodule.

Results

Table 28 presents the steps taken in the refinement of the initial 388protein candidates down to the set of 13 classifier proteins used forvalidation and performance assessment. The results are presented in thesame sequence.

TABLE 28 Steps in refining the 388 candidates down to the 13-proteinclassifier Number of Proteins Refinement 388 Lung cancer associatedprotein candidates sourced from tissue and literature. 371 Number of the388 protein candidates successfully developed into a SRM assay. 190Number of the 371 SRM protein assays detected in plasma. 125 Number ofthe 190 SRM protein assays detected in at least 50% of cancer or 50% ofbenign discovery samples. 36 Number of the 125 detected proteins thatwere cooperative. 21 Number of the 36 cooperative proteins with robustSRM assays (i.e. no interfering signals, good signal-to-noise, etc.) 13Number of the 21 robust and cooperative proteins with stable logisticregression coefficients.

Selection of Biomarker Candidates for Assay Development.

To identify lung cancer biomarkers in blood that are shed or secretedfrom lung tumor cells, proteins overexpressed on the cell surface orover-secreted from lung cancer tumor cells relative to normal lung cellswere identified from freshly resected lung tumors using organelleisolation techniques combined with mass spectrometry. In addition, anextensive literature search for lung cancer biomarkers was performedusing public and private resources. Both the tissue-sourced biomarkersand literature-sourced biomarkers were required to have evidence ofprevious detection in blood. The tissue (217) and literature (319)candidates overlapped by 148 proteins, resulting in a list of 388protein candidates.

Development of SRM Assays.

Standard synthetic peptide techniques were used to develop a 371-proteinmultiplexed SRM assay from the 388 protein candidates. For 17 of thecandidates, appropriate synthetic peptides could not be developed orconfidently identified. The 371 SRM assays were applied to plasmasamples from patients with pathologically confirmed benign nodules andpathologically confirmed malignant lung nodules to determine how many ofthe 371 proteins could be detected in plasma. A total of 190 SRM assayswere able to detect their target proteins in plasma (51% success rate).This success rate (51%) compares very favorably to similar efforts (16%)to develop large scale SRM assays for the detection of diverse cancermarkers in blood. Of the 190 proteins detected in blood, 114 werederived from the tissue-sourced candidates and 167 derived from theliterature-sourced candidates (91 protein overlap). It is conjecturedthat the 49% of candidate proteins not detected in blood were present,but below the level of detection of the technology.

Classifier Discovery.

A summary of the features of the 143 samples used for classifierdiscovery appears in Table 29. Samples were obtained from three clinicalsites to avoid overfitting to a single clinical site. Participatingclinical sites were Institut Universitaire de Cardiologie et dePneumologie de Quebec (IUCPQ), New York University (NYU) and Universityof Pennsylvania (UPenn). All samples were selected to be consistent withintended use, specifically, having nodule size 30 mm or less. Cancer andbenign samples were pathologically confirmed.

TABLE 29 Clinical characteristics of subjects and nodules in thediscovery and validation studies Characteristics Cancer n Benign n pvalue Cancer n Benign n p value Discovery Study Validation StudySubjects 72 71 52 52 Age (year)* 65 64 0.46^(†) 63 62 0.03^(†) (59-72)(52-71) (60-73) (56-67) Gender 1.00^(‡) 0.85^(‡) Male 29 28 25 27 Female43 43 27 25 Smoking History Status 0.006^(‡) 0.006^(‡) Never^(§) 5 19 315 Former 60 44 38 29 Current 6 6 11 7 No Data 1 2 0 1 Pack-Year*^(¶) 3720 0.001^(†) 40 27 0.09^(†) (20-52)  (0-40) (19-50)  (0-50) Nodules Size(mm)* 13 13 0.69^(†) 16 15 0.68^(†) (10-16) (10-18) (13-20) (12-22)Source 1.00^(‡) 0.89^(‡) IUCPQ^(||) 14 14 13 12 New York 29 28 6 9Pennsylvania 29 29 14 13 Vanderbilt 0 0 19 18 Histopathology BenignDiagnosis Granuloma — 48 — 26 Hamartoma — 9 — 6 Scar — 2 — 2 Other** —12 — 18 Cancer Diagnosis Adenocarcinoma 41 — 25 — Squamous Cell 3 — 15 —Large Cell 0 — 2 — Bronchioloalveolar 3 — 0 — (BAC) Adenocarcinoma/BAC21 — 5 — Other^(††) 4 — 5 — *Data shown are median values with quartileranges indicated in parentheses. ^(†)Mann-Whitney test. ^(‡)Fisher'sexact test. ^(§)A never smoker is defined as an individual who has alifetime history of smoking less than 100 cigarettes. ^(¶)A pack-year isdefined as the product of the total number of years of smoking and theaverage number of packs of cigarettes smoked daily. Pack-year data werenot available for 4 cancer and 6 benign subjects in the discovery setand 2 cancer and 3 benign subjects in the validation set. ^(||)IUCPQ isthe Institute Universitaire de Cardiologie et de Pneumologie de Quebec.**For the discovery study, the Benign Diagnosis “Other” categoryincluded: amyloidosis, n = 2; fibroelastic nodule, n = 1; fibrosis, n =1; hemorrhagic infarct, n = 1; lymphoid aggregate, n = 1; organizingpneumonia, n = 3; pulmonary infarct, n = 1; sclerosing hemangioma, n =1; and subpleural fibrosis with benign lymphoid hyperplasia, n = 1. Forthe validation study, the Benign Diagnosis “Other” category included:amyloidosis, n = 1; bronchial epithelial cells, n = 4; bronchiolitisinterstitial fibrosis, n = 1; emphysematous lung, n = 1; fibroticinflammatory lesion, n = 1; inflammation, n = 1; parenchymalintussusception, n = 1; lymphangioma, n = 1; mixed lymphocytes andhistiocytes, n = 1; normal parenchyma, n = 1; organizing pneumonia, n =1; pulmonary infarct, n = 2; respiratory bronchiolitis, n = 1; andsquamous metaplasia, n = 1. ^(††)For the discovery study, the non-smallcell lung cancer (NSCLC) Diagnosis “Other” category included:adenocarcinoma squamous cell mixed, n = 1; large cell squamous cellmixed, n = 1; pleomorphic carcinoma, n = 1, and not specified, n = 1.For the validation study, the NSCLC Diagnosis “Other” category included:carcinoid, n = 2; large cell squamous cell mixed, n = 1; and notspecified, n = 2.

Benign and cancer samples were paired by matching on age, gender, nodulesize and clinical site to avoid bias during SRM analysis and also toensure that the biomarkers discovered were not markers of age, gender,nodule size or clinical site.

The 371-protein SRM assay was applied to the 143 discovery samples andthe resulting transition data were analyzed to derive a 13-proteinclassifier using a logistic regression model (Table 30). The key step inthis refinement (Table 28) was the identification of 36 cooperativeproteins of which 21 had robust SRM signal. A protein was deemedcooperative if found more frequently on the best performing panels thanexpected by chance alone, with the significance determined using thefollowing statistical estimation procedure. Briefly, a million random10-protein panels were generated and the frequency of each protein amongthe best performing panels (p value ≦10⁴) was calculated. These proteinswere sampled from the list of 125 proteins reproducibly detected ineither benign samples or in cancer samples (see Table 28). Full detailsof the estimation procedure and the full discovery process are describedin Materials and Methods in Example 9. Importantly, the 13-proteinclassifier was fully defined before validation was performed.

TABLE 30 The 13-protein logistic regression classifier Protein (Human)Transition Coefficient LRP1 TVLWPNGLSLDIPAGR_855.00_400.20 −1.59 BGH3LTLLAPLNSVFK_658.40_804.50 1.73 COIA1 AVGLAGTFR_446.26_721.40 −1.56 TETNLDTLAQEVALLK_657.39_330.20 −1.79 TSP1 GFLLLASLR_495.31_559.40 0.53 ALDOAALQASALK_401.25_617.40 −0.80 GRP78 TWNDPSVQQDIK_715.85_260.20 1.41 ISLRALPGTPVASSQPR_640.85_841.50 1.40 FRIL LGGPEAGLGEYLFER_804.40_913.40 0.39LG3BP VEIFYR_413.73_598.30 −0.58 PRDX1 QITVNDLPVGR_606.30_428.30 −0.34FIBA NSLFEYQK_514.76_714.30 0.31 GSLG1 IIIQESALDYR_660.86_338.20 −0.70Constant (α) equals to 36.16.

Classifier Validation.

A total of 52 cancer and 52 benign samples (Table 29) were used tovalidate the performance of the 13-protein classifier. All validationsamples were from different patients than the discovery samples. Inaddition, 36% of the validation samples were sourced from a new fourthclinical site, Vanderbilt University (Vanderbilt). A new clinical siteparticipating in the validation study provides greater confidence thatthe classifier's performance generalizes beyond the discovery study. Theremaining validation samples were selected randomly from the discoverysites. Samples were selected to be consistent with intended use andmatched as in the discovery study.

The classifier was applied to the validation samples and analyzed(Materials and Methods in Example 9). The performance of the classifieris presented in FIG. 12 in terms of negative predictive value (NPV) andspecificity (SPC), as these are the two most clinically relevantmeasures. NPV is the population-based probability that a nodulepredicted to be benign by the classifier is truly benign. As the NPV isrepresentative of the classifier's performance on the intended usepopulation, it can be calculated from the classifier's sensitivity,specificity and the estimated cancer prevalence (20%) in the intendeduse population. Specificity is the percentage of benign nodules that arepredicted to be benign by the classifier. The classifier generates acancer probability score, ranging from 0 to 1. Any reference value inthis range can be defined so that a sample is predicted to be benign ifthe sample's classifier score is below the reference value, or predictedto be malignant if the sample's classifier score is above the referencevalue. The reference value used in practice depends primarily on thephysician and his/her minimum required NPV. For the purposes ofillustration we assume that the NPV requirement is 90%.

At reference value 0.43, the classifier has NPV of 96%+/−4% andspecificity of 45%+/−13% on the discovery samples, where 95% confidenceintervals are reported. At the same reference value of 0.43, theclassifier has NPV of 92%+/−7% and specificity of 27%+/−12% on thevalidation samples. Table 31 reports the classifier's performance fordiscovery and validation sample sets and for multiple lung cancerprevalences. For each lung cancer prevalence, the reference value wasselected to ensure NPV is 90% or more.

TABLE 31 Performance of the classifier in discovery and validation atthree cancer prevalences Prevalence Reference Sensitivity SpecificityNPV PPV Dataset (%) Value (%) (%) (%) (%) Discovery 20 0.43 93 45 96 30(n = 143) 25 0.37 96 38 96 34 30 0.33 96 34 95 38 Validation 20 0.43 9027 92 24 (n = 104) 25 0.37 92 23 90 29 30 0.33 94 21 90 34 Vander 200.43 100 28 100 26 bilt 25 0.37 100 22 100 30 (n = 37) 30 0.33 100 17100 34 NPV is negative predictive value. PPV is positive predictivevalue.

The performance of the 13-protein classifier on validation samples fromthe new clinical site (Vanderbilt) is a great indicator of theclassifier's performance on future samples, and a strong sign that theclassifier is not overfit to the three discovery sites. The NPV andspecificity on the Vanderbilt samples are 100% and 28%, respectively, atthe same reference value 0.43.

FIG. 13 presents the application of the classifier to all 247 discoveryand validation samples. FIG. 13 compares the clinical risk factors ofsmoking (measured in pack years) and nodule size (proportional to thediameter of each circle) to the classifier score assigned to eachsample. Nodule size does not appear to increase with the classifierscore. Indeed, both large and small nodules are spread across theclassifier score spectrum. To quantify this observation, the Pearsoncorrelation between the classifier score and nodule size, smokinghistory pack-year and age were calculated and found to be insignificant(Table 32). The implication of this observation is remarkable. Theclassifier provides information on the disease status of a pulmonarynodules that is independent of the three currently used risk factors formalignancy (age, smoking history and nodule size), and thus providesincremental molecular information of great added clinical value. For asimilar plot of nodule size vs. classifier score, see FIG. 15.

TABLE 32 Impact of clinical characteristics on classifier scoreContinuous Clinical Characteristics Sample Pearson Coefficient of 95%CI* of p-value on Characteristics Group Correlation Linear FitCoefficient Coefficient Subject Age All 0.190 0.005  (0.002, −0.008)0.003 Cancer 0.015 0.000 (−0.004, −0.004) 0.871 Benign 0.227 0.005 (0.001, −0.010) 0.012 Smoking All 0.185 0.002  (0.000, −0.003) 0.005History Cancer 0.089 0.001 (−0.001, −0.002) 0.339 Pack-Years Benign0.139 0.001  (0.000, −0.003) 0.140 Nodule Size All −0.071 −0.003(−0.008, −0.002) 0.267 Cancer −0.081 −0.003 (−0.009, −0.003) 0.368Benign −0.035 −0.001 (−0.008, −0.005) 0.700 Categorical ClinicalCharacteristics Classifier p-value on p-value on Characteristics ScoreCancer Cancer Benign Benign Gender 0.477† 0.110† Female Median 0.7860.479 (quartile range) (0.602-0.894) (0.282-0.721) Male Median 0.8150.570 (quartile range) (0.705-0.885) (0.329-0.801) Smoking 0.652† 0.539†History Status Never Median 0.707 0.468 (quartile range) (0.558-0.841)(0.317-0.706) Past Median 0.804 0.510 (quartile range) (0.616-0.892)(0.289-0.774) Current Median 0.790 0.672 (quartile range) (0.597-0.876)(0.437-0.759)

The Molecular Foundations of the Classifier.

To address the biological relevance of the 13 classifier proteins, theywere submitted for pathway analysis using IPA (Ingenuity Systems,www.ingenuity.com). It is identified that the transcription regulatorsmost likely to cause a modulation of these 13 proteins. Using standardIPA analysis parameters, the four most significant (see Materials andMethods in Example 9) nuclear transcription regulators were FOS(proto-oncogene c-Fos), NF2L2 (nuclear factor erythroid 2-related factor2), AHR (aryl hydrocarbon receptor) and MYC (myc proto-oncogeneprotein). These proteins regulate 12 of the 13 classifier proteins, withISLR being the exception (see below).

FOS is common to many forms of cancer. NF2L2 and AHR are associated withlung cancer, oxidative stress response and lung inflammation. MYC isassociated with lung cancer and oxidative stress response. These fourtranscription regulators and the 13 classifier proteins, collectively,are also highly associated (p-value 1.0e-07) with the same threebiological networks, namely, lung cancer, lung inflammation andoxidative stress response. This is summarized in FIG. 14 where theclassifier proteins (green), transcription regulators (blue) and thethree merged networks (orange) are depicted. Only ISLR (Immunoglobulinsuperfamily containing leucine-rich repeat protein) is not connectedthrough these three networks to other classifier proteins, although itis connected through cancer networks not specific to lung. In summary,the modulation of the 13 classifier proteins can be linked back to a fewtranscription regulators highly associated with lung cancer, lunginflammation and oxidative stress response networks; three biologicalprocesses reflecting aspects of lung cancer.

The present invention distinguishes itself in multiple ways. First, theperformance of the 13-protein classifier achieves intended useperformance requirements with NPV (and sensitivity) of at least 90% orhigher in validation, across multiple prevalence estimates (see Table31). Second, intended use population samples (nodule size 30 mm or lessand/or Stage IA) were used in discovery and validation, in contrast toprior studies where non-intended use samples ranging from Stage I toStage IV were used. In some cases, nodule size information was notdisclosed in prior work. Third, the 13-protein classifier wasdemonstrated to provide a score that is independent of the currentlyused cancer risk parameters of nodule size, smoking history and age.

The utilization of SRM technology enables global interrogation ofproteins associated with lung cancer processes in contrast totechnologies such as those that multiplex antibodies where it is oftennot feasible to multiplex hundreds of candidate markers for a specificdisease.

Clinical Study Designs.

The design and conduct of biomarker studies is necessarily impacted bythe eventual intended use population and performance requirements forthe clinical test. Emerging guidelines help in the design of studiesthat have greater chance of translating into clinical impact. In thedesign of the discovery and validation studies presented here, fourrequirements were especially important. First, conducting a multipleclinical site discovery study enabled us to determine those proteinsrobust to variations introduced by differences in site-to-site sampleprocessing and management, as well as from any biological differences inthe populations being served by the different site hospitals. Such adesign is critical as site-to-site sources of variations can oftenexceed biological signal. Second, utilizing intended use samples, asdefined by age, smoking history and nodule size, in discovery andvalidation phases enabled us to obtain a realistic estimate of theperformance envelop of the classifier. Third, careful matching of cancerand benign cohorts on age, gender, nodule size and clinical site wascritical in not only avoiding bias, but in the discovery and validationof a classifier that provides a score independent of these clinicalfactors as well as smoking history. Fourth, validation samples were fromdifferent patients than the discovery samples. Furthermore, 36% of thevalidation samples were from an entirely new clinical site, a criticalvalidation step to show that results are not overfit to the sites usedin the discovery phase. Performance on samples from the new clinicalsite was exceptionally high (NPV of 100%, specificity of 28%), yieldinga high level of confidence in the performance of the test in clinicalpractice.

Systems Biology and Blood Signatures.

The integration of a systems biology approach to biomarker discoverywith SRM technology enabled the simultaneous exploration of a largenumber of lung cancer relevant proteins, resulting in a highly sensitiveclassifier. The systems approach employed several strategies.

First, proteins secreted or shed from the cell surface of lung cancercells were identified (i.e. tissue-sourced) as these are likely lungcancer perturbed proteins to be detected in blood. Of the classifier's13 proteins, seven were tissue-sourced, demonstrating thattissue-sourcing is an effective method for prioritizing proteins for SRMassay development.

A second systems driven approach was the identification of the mostcooperative protein biomarkers. Cooperative proteins are those that maynot be the best individual performers but appear frequently on highperformance panels. Motivating this approach is the desire to derive aclassifier with multiple proteins from multiple lung cancer associatednetworks. By monitoring multiple proteins and networks, it was expectedthat the classifier would be highly sensitive to the circulatingsignature of a malignant nodule, as demonstrated in validation.

There are two confirmations of the effectiveness of the cooperativeprotein approach. A pathway analysis demonstrated that the classifierproteins are likely modulated by a small number of transcriptionregulators (AHR, NF2L2, MYC, FOS) highly associated with lung cancer,lung inflammation and oxidative stress response networks/processes.Chronic lung inflammation and oxidative stress response are both linkedto NSCLC development. A strength of the classifier is that it monitorsmultiple proteins from these multiple lung cancer associated processes.This multiple protein, multiple process survey accounts for the highsensitivity of the classifier for detecting the circulating signatureemitted by malignant nodules, and so, high NPV when the classifier callsa nodule benign.

The second validation of the cooperative approach is a direct comparisonto traditional biomarker strategies. Typically, proteins are shortlistedin the discovery process by filtering on individual diagnosticperformance. To contrast the difference between filtering proteins basedon strong individual performance as opposed to frequency on highperformance panels, we calculated a p-value for each protein using theMann-Whitney non-parametric test. Only 2 of the 36 cooperative proteinshad a p-value below 0.05, a commonly used significance threshold formeasuring individual performance. More importantly, we derived a“p-classifier” using the same steps for the 13-protein classifierderivation (see Table 28 and Materials and Methods in Example 9) exceptthat the Mann Whitney p-value was used in place of cooperative score.The p-classifier achieved NPV 96% and specificity 18% in discovery andNPV 91% and specificity 19% in validation as compared to the 13-proteinclassifier performance of NPV 96% and specificity 45% in discovery andNPV 92% and specificity 27% in validation. Note that the reference valuethresholds were selected to ensure NPV of at least 90%. Hence, we expectsimilar high NPV performance between the 13-protein cooperativeclassifier and the p-classifier. Specificity is the performance measurewhere a comparison can be made. This is where a significant drop inperformance from the 13-protein cooperative classifier to thep-classifier is observed. This confirms that the best individual proteinperformers are not necessarily the best proteins for classifiers

Most Informative Proteins.

Which proteins in the classifier are most informative? To answer thisquestion all possible classifiers were constructed from the set ofrobust cooperative proteins and their performance measured. Thefrequency of each protein among the 100 best performing panels wasdetermined. Four proteins (LRP1, COIA1, ALDOA, LG3BP) were highlyenriched with 95% of the 100 best classifiers having at least three ofthese four proteins (p-value <1.0e-100). Seven of eight proteins (LRP1,COIA1, ALDOA, LG3BP, BGH3. PRDX1, TETN, ISLR) appeared together on overhalf of all the best classifiers (p-value <1.0e-100). Note that the13-protein classifier contains additional proteins as they furtherincrease performance, likely by measuring proteins in the same threelung cancer networks (lung cancer, lung inflammation and oxidativestress). The conclusion is that high performance panels of cooperativeproteins for pulmonary nodule characterization are similar incomposition to one another with a preference for a set of particularlyinformative (cooperative) proteins.

In summary, by integrating systems biology strategies for biomarkerdiscovery (tissue-sourced candidates with cancer relevance, cooperativeproteins, multiple proteins from multiple lung cancer associatednetworks), enabling technologies (SRM for global proteomicinterrogation) and clinical focus (designing studies for intended use),this invention identifies a 13-protein proteomic classifier thatprovides molecular insight into the disease status of pulmonary nodules.

Example 9 Materials and Methods

Identification of Candidate Plasma Proteins.

Two approaches were employed to identify candidate proteins for a lungcancer classifier, including analysis of the proteome of lung tissueswith a histopathologic diagnosis of NSCLC and a search of literaturedatabases for lung cancer-associated proteins. All candidate proteinswere also assessed for evidence of blood circulation and satisfied oneor more requirement(s) for the evidence.

Analysis of Plasma Samples Using SRM-MS.

Briefly, the protocol for SRM-MS analysis of plasma aliquots includedimmunodepletion on IgY14-Supermix resin columns (Sigma) of medium- andhigh-abundance proteins, denaturation, trypsin digestion, and desalting,followed by reversed-phase liquid chromatography and SRM-MS analysis ofthe obtained peptide samples.

Development of SRM Assays.

SRM assays for candidate proteins were developed based on syntheticpeptides, as previously described. After identification and synthesis ofup to five suitable peptides per protein, SRM triggered MS/MS spectrawere collected on a 5500 QTrap® mass spectrometer for both doubly andtriply charged precursor ions. The obtained MS/MS spectra were assignedto individual peptides using MASCOT and with a minimum cutoff score of15. Up to four transitions per precursor ion were then selected foroptimization. The resulting corresponding optimal retention time,declustering potential and collision energy were assembled for alltransitions. Optimal transitions were measured on a mixture of allsynthetic peptides and on two pooled plasma samples, each obtained fromten subjects with either benign or malignant, i.e. NSCLC, lung nodulesat the Institut Universitaire de Cardiologie et de Pneumologie de Quebec(IUCPQ, Quebec, Canada). All subjects provided informed consent andcontributed biospecimens in studies approved by the institution's EthicsReview Board (ERB). Plasma samples were processed as described above.Batches of 1750 transitions were analyzed by SRM-MS, with SRM-MS datamanually reviewed to select the two best peptides per protein and thetwo best transitions per peptide. The intensity ratio, defined as theratio between the intensities of the two best transitions of a peptidein the synthetic peptide mixture, was used to assess the specificity ofthe transitions in a biological sample. Transitions demonstratinginterference with other transitions were not selected. A method toensure the observed transitions corresponded to the peptides andproteins they were intended to measure was developed. In particular, 93%of peptide transitions developed had an error rate below 5%.

Discovery Study Design.

A retrospective, multi-center, case-control study was performed usingarchival K2-EDTA plasma aliquots previously obtained from subjects whoprovided informed consent and contributed biospecimens in studiesapproved by the Ethics Review Board (ERB) or the Institutional ReviewBoards (IRB) at the IUCPQ or New York University (New York, N.Y.) andthe University of Pennsylvania (Philadelphia, Pa.), respectively. Inaddition, plasma samples were provided by study investigators afterreview and approval of the sponsor's study protocol by the respectiveinstitution's ERB or IRB, as required. Sample eligibility for theproteomic analysis was based on the satisfaction of the study inclusionand exclusion criteria, including the subject's demographic information;the subject's corresponding lung nodule radiographic characterization bychest CT scan and a maximal linear dimension of 30 mm; and thehistopathology of the lung nodule obtained at the time of diagnosticsurgical resection, i.e. either NSCLC or a benign, i.e. non-malignant,process. Each cancer-benign sample pair was matched, as much as possibleamong eligible samples, by gender, nodule size (±10 mm), age (±10years), smoking history pack-years (±20 pack-years), and by center.Independent monitoring and verification of the clinical data associatedwith both the subject and lung nodule were performed in accordance withthe guidance established by the Health Insurance Portability andAccountability Act (HIPAA) of 1996 to ensure subject privacy. The studywas powered with a probability of 92% to detect 1.5 fold differences inprotein abundance between malignant and benign lung nodules.

Logistic Regression Model.

The logistic regression classification method was used to combine apanel of transitions into a classifier and to calculate a classificationprobability score between 0 and 1 for each sample. The probability score(P_(s)) of a sample was determined as

P _(s)=1/[1+exp(−α−Σ_(i=1) ^(N)β_(i) *{hacek over (I)} _(i,s))],  (1)

where {hacek over (I)}_(i,s) was the logarithmically transformed (base2), normalized intensity of transition i in sample s, β_(i) was thecorresponding logistic regression coefficient, α was aclassifier-specific constant, and N was the total number of transitionsin the classifier. A sample was classified as benign if P_(s) was lessthan a reference value or cancer otherwise. The reference value can beincreased or decreased depending on the desired NPV. To define theclassifier, the panel of transitions (i.e. proteins), theircoefficients, the normalization transitions, classifier coefficient αand the reference value must be learned (i.e. trained) from thediscovery study and then confirmed using the validation study.

Lung Nodule Classifier Development. The goal of the discovery study wasto derive a multivariate classifier with a target performance sufficientfor clinical utility in the intended use population, i.e. a classifierhaving an NPV of 90% or higher. This goal was incorporated in the dataanalysis strategies. The classifier development included the following:normalization and filtering of raw SRM-MS data; identification ofcandidate proteins that occurred with a high frequency in top-performingpanels; evaluation of candidate proteins based on SRM-MS signal quality;selection of candidate proteins for the final classifier based on theirstability in performance; and training to a logistic regression model toderive the final classifier. Table 28 provides a summary overview of theprimary steps.

Normalization of raw SRM-MS data was performed to reducesample-to-sample intensity variations using a panel of six endogenousproteins. After data normalization, SRM-MS data were filtered down totransitions having the highest intensities of the corresponding proteinsand satisfying the criterion for detection in a minimum of 50% of thecancer or 50% of the benign samples. A total of 125 proteins satisfiedthese criteria of reproducible detection. Missing values were replacedby half the minimum detected values of the corresponding transitions inall samples.

Remaining transitions were then used to identify proteins, defined ascooperative proteins, that occurred with high frequency ontop-performing protein panels. The cooperative proteins were derivedusing the following estimation procedure as it is not computationalfeasible to evaluate the performance of all possible protein panels.

Monte Carlo cross validation (MCCV) (36) was performed on 1×10⁶ panels,each panel comprised of 10 randomly selected proteins and fitted to alogistic regression model, as described above, using a 20% holdout rateand 10² sample permutations. The receiver operating characteristic (ROC)curve of each panel was generated and the corresponding partial areaunder the ROC curve (AUC) but above the boundary of sensitivity being90%, defined as the partial AUC (37, 38), was used to assess theperformance of the panel. By focusing on the performance of individualpanels at high sensitivity region, the partial AUC allows for theidentification of panels with high and reliable performance on NPV. Thecandidate proteins that occurred in the top 100 performing panels with afrequency greater than that expected by chance were identified ascooperative proteins. For each protein the cooperative score is definedas its frequency on the 100 high performance panels divided by theexpected frequency. Highly cooperative proteins had a score of 1.75 orhigher (the corresponding one-sided p value <0.05) while non-cooperativeproteins had a score of 1 or less. Note that one million panels weresampled to ensure that the 100 top performing panels were exceptional(empirical p value ≦10⁻⁴). In addition, panels of size 10 were used inthis procedure based on empirical evidence that larger panels did notchange the resulting list of cooperative proteins. We also wanted toavoid overfitting the logistic regression model. In total, 36cooperative proteins were identified, including 15 highly cooperativeproteins.

Raw chromatograms of all transitions of cooperative proteins were thenmanually reviewed. Proteins with low signal-to-noise ratios and/orshowing evidence of any interference were removed from furtherconsideration for the final classifier. In total, 21 cooperative androbust proteins were identified.

Remaining candidate proteins were then evaluated in an iterative,stepwise procedure to derive the final classifier. In each step, MCCVwas performed using a holdout rate of 20% and 104 sample permutations totrain the remaining candidate proteins to a logistic regression modeland to assess the variability, i.e. stability, of the coefficientderived for each protein by the model. The protein having the leaststable coefficient was identified and removed. Proteins for the finalclassifier were identified when the corresponding partial AUC wasoptimal. Seven of the 13 proteins in the final classifier were highlycooperative.

Proteins in the final classifier were further trained to a logisticregression model by MCCV with a holdout rate of 20% and 2×10⁴ samplepermutations.

Lung Nodule Classifier Validation.

The design of the validation study was identical to that of thediscovery study, but involved K2-EDTA plasma samples associated withindependent subjects and independent lung nodules not evaluated in thediscovery study. Additional specimens were obtained from VanderbiltUniversity (Nashville, Tenn.) with similar requirements for patientconsent, IRB approval, and satisfaction of HIPAA requirements. Of the104 total cancer and benign samples in the validation study, half wereanalyzed immediately after the discovery study, while the other half wasanalyzed later. The study was powered to observe the expected 95%confidence interval (CI) of NPV being 90±8%.

The raw SRM-MS dataset in the validation study was normalized in thesame way as the discovery dataset. Variability between the discovery andthe validation studies was mitigated by utilizing human plasma standard(HPS) samples in both studies as external calibrator. Missing data inthe validation study were then replaced by half the minimum detectedvalues of the corresponding transitions in the discovery study.Transition intensities were applied to the logistic regression model ofthe final classifier learned previously in the training phase, fromwhich classifier scores were assigned to individual samples. Theperformance of the lung nodule classifier on the validation samples wasthen assessed based on the classifier scores.

IPA Pathway Analysis.

Standard parameters were used. Specifically, in the search for nucleartranscription regulators, requirements were p-value <0.01 with a minimumof 3 proteins modulated. Significance was determined using aright-tailed Fisher's exact test using the IPA Knowledge Database asbackground.

Candidate Biomarker Identification.

Candidate Biomarkers Identified by Tissue Proteomics.

Specimens of resected NSCLC (adenocarcinoma, squamous cell and largecell) lung tumors and non-adjacent normal tissue in the same lobe wereobtained from patients who provided informed consent in studies approvedby the Ethics Review Boards at the Centre Hospitalier de l'Université deMontréal and the McGill University Health Centre.

The proteomic analyses of lung tumor tissues targetedmembrane-associated proteins on endothelial cells (adenocarcinoma, n=13;squamous cell, n=18; and large cell, n=7) and epithelial cells(adenocarcinoma, n=19; squamous cell, n=6; and large cell, n=5), andthose associated with the Golgi apparatus (adenocarcinoma, n=13;squamous cell, n=15; and large cell, n=5).

Membrane proteins from endothelial cells or epithelial cells andsecreted proteins were isolated from normal or tumor tissues from freshlung resections after washing in buffer and disruption with a Polytronto prepare homogenates. The cell membrane protocol included filtrationusing 180 μm mesh and centrifugation at 900×g for 10 min at 4° C.,supernatants prior to layering on 50% (w:v) sucrose and centrifugationat 218,000×g for 1 h at 4° C. to pellet the membranes. Membrane pelletswere resuspended and treated with micrococcal nuclease, and incubatedwith the following antibodies specified by plasma membrane type:endothelial membranes (anti-thrombomodulin, anti-ACE, anti-CD34 andanti-CD144 antibodies); epithelial membranes (anti-ESA, anti-CEA,anti-CD66c and anti-EMA antibodies), prior to centrifugation on top of a50% (w:v) sucrose cushion at 280,000×g (endothelial) or 218,000×g(epithelial) for 1 h at 4° C. After pellet resuspension, plasmamembranes were isolated using MACS microbeads. Endothelial plasmamembranes were treated with KI to remove cytoplasmic peripheralproteins. The eluate of epithelial plasma membranes was centrifuged at337,000×g for 30 min at 4° C. over a 33% (w:v) sucrose cushion, withresuspension of the pellet in Laemmli/Urea/DTT after removal of thesupernatant and sucrose cushion.

To isolate secreted tissue proteins, the density of the tissuehomogenates (prepared as described above) was adjusted to 1.4 M sucroseprior to isolating the secretory vesicles by isopycnic centrifugation at100,000×g for 2 h at 4° C. on a 0.8 and 1.2 M discontinuous sucrosegradient. Vesicles concentrating at the 0.8/1.2 M interface werecollected and further incubated for 25 min with 0.5 M KCl to removeloosely bound peripheral proteins. Vesicles were recuperated byultracentrifugation at 150,000×g for 1 h at 4° C. and then opened with100 mM (NH₄)HCO₃ (pH 11.0) for 30 min at 4° C. Secreted proteins wererecovered in the supernatant following ultracentrifugation at 150,000×gfor 1 h at 4° C.

Membrane or secreted proteins were then analyzed by CellCarta® (Caprion,Montréal, Québec) proteomics platform, including digestion by trypsin,separation by strong cation exchange chromatography, and analysis byreversed-phase liquid chromatography coupled with electrospray tandemmass spectrometry (MS/MS). Peptides in the samples were identified bydatabase searching of MS/MS spectra using MASCOT and quantified by alabel-free approach based on their signal intensity in the samples,similar to those described in the literature. Proteins whosetumor-to-normal abundance ratio was either ≧1.5 or ≦2/3 were thenidentified as candidate biomarkers.

Candidate Biomarkers Identified by Literature Searches.

Automated literature searches using predefined terms and automated PERLscripts were performed on the following databases: UniProt(www.uniprot.org) on May 6, 2010, Entrez(www.ncbi.nlm.nih.gov/books/NBK3836) on May 17, 2010, and NextBio(www.nextbio.com) on Jul. 8, 2010. Biomarker candidates were compiledand mapped to UniProt identifiers using the UniProt Knowledge Base(http://www.uniprot.org/help/uniprotkb).

Presence of Candidate Biomarkers in the Blood.

The tissue- and literature-identified biomarker candidates were requiredto demonstrate documented evidence in the literature or a database as asoluble or solubilized circulating protein. The first criterion wasevidence by mass spectrometry detection, with a candidate designated aspreviously detected by the following database-specific criteria: aminimum of 2 peptides in HUPO9504, which contains 9,504 human proteinsidentified by MS/MS; a minimum of 1 peptide in HUPO889, which is ahigher confidence subset of HUPO9504 containing 889 human proteins; orat least 2 peptides in Peptide Atlas (November 2009 build). The secondcriterion was annotation as either a secreted or single-pass membraneprotein in UniProt. The third criterion was designation as a plasmaprotein in the literature. The fourth criterion was prediction as asecreted protein based on the use of various programs: prediction byTMHMM as a protein with one transmembrane domain, which however iscleaved based on prediction by SignalP; or prediction by TMHMM as havingno transmembrane domain and prediction by either SignalP or SecretomePas a secreted protein. All candidate proteins satisfied one or more ofthe criteria.

Study Designs and Power Analyses.

Sample, Subject and Lung Nodule Inclusion and Exclusion Criteria.

The inclusion criteria for plasma samples were collection inEDTA-containing blood tubes; obtained from subjects previously enrolledin the Ethics Review Board (ERB) or the Institutional Review Boards(IRB) approved studies at the participating institutions; and archived,e.g. labeled, aliquoted and frozen, as stipulated by the studyprotocols.

The inclusion criteria for subjects were the following: age ≧40; anysmoking status, e.g. current, former, or never; any co-morbidconditions, e.g. chronic obstructive pulmonary disease (COPD); any priormalignancy with a minimum of 5 years in clinical remission; any priorhistory of skin carcinomas, e.g. squamous or basal cell. The onlyexclusion criterion was prior malignancy within 5 years of lung nodulediagnosis.

The inclusion criteria for the lung nodules included radiologic,histopathologic and staging parameters. The radiologic criteria includedsize ≧4 mm and ≦30 mm, and any spiculation or ground glass opacity. Thehistopathologic criteria included either diagnosis of malignancy, e.g.non-small cell lung cancer (NSCLC), including adenocarcinoma (andbronchioloalveolar carcinoma (BAC), squamous, or large cell, or a benignprocess, including inflammatory (e.g. granulomatous, infectious) ornon-inflammatory (e.g. hamartoma) processes. The clinical stagingparameters included: primary tumor: ≦T1 (e.g. 1A and 1B); regional lymphnodes: N0 or N1 only; distant metastasis: M0 only. The exclusioncriteria for lung nodules included the following: nodule size dataunavailable; no pathology data available, histopathologic diagnosis ofsmall cell lung cancer; and the following clinical staging parameters:primary tumor: ≧T2, regional lymph nodes: ≧N2, and distant metastasis:≧M1.

Sample Layout.

Up to 15 paired samples per batch were assigned randomly and iterativelyto experimental processing batches until no statistical bias wasdemonstrable on age, gender or nodule size. Paired samples within eachprocessing batch were further randomly and repeatedly assigned topositions within the processing batch until the absolute values of thecorresponding Pearson correlation coefficients between position and age,gender and nodule size were less than 0.1. Each pair of cancer andbenign samples was then randomized to their relative positions in thebatch. To provide a positive control for quality assessment, three 200μl aliquots of a pooled human plasma standard (HPS) (Bioreclamation,Hicksville, N.Y.) were positioned at the beginning, middle and end ofeach processing batch, respectively. Samples within a batch wereanalyzed together: sequentially during immunodepletion and SRM-MSanalysis but in parallel during denaturing, digestion, and desalting.

Power Analysis for the Classifier Discovery Study.

The power analysis for the discovery study was based on the followingassumptions: (A) The overall false positive rate (α) was set to 0.05.(B) {hacek over (S)}idák correction for multiple testing was used tocalculate the effective α_(eff) for testing 200 proteins,

${i.e.\mspace{14mu} \alpha_{eff}} = {1 - {\sqrt[200]{1 - \alpha}.}}$

(C) The effective sample size was reduced by a factor of 0.864 toaccount for the larger sample requirement for the Mann-Whitney test thanfor the t-test (13). (D) The overall coefficient of variation was set to0.43 based on a previous experience. (E) The power (1-β) of the studywas calculated based on the formula for the two-sample, two-sidedt-test, using effective α_(eff) and effective sample size.

Power Analysis for the Classifier Validation Study.

Sufficient cancer and benign samples are needed in the validation studyto confirm the performance of the lung nodule classifier obtained fromthe discovery study. We are interested in obtaining the 95% confidenceintervals (CIs) on NPV and specificity for the classifier. Assuming thecancer prevalence of lung nodules is prev, the negative predictive value(NPV) and the positive predictive value (PPV) of a classifier on thepatient population with lung nodules were calculated from sensitivity(sens) and specificity (spec) as follows:

$\begin{matrix}{{NPV} = \frac{\left( {1 - {prev}} \right)*{spec}}{{{prev}*\left( {1 - {sens}} \right)} + {\left( {1 - {prev}} \right)*{spec}}}} & {S\; 1} \\{{PPV} = \frac{{prev}*{sens}}{{{prev}*{sens}} + {\left( {1 - {prev}} \right)*\left( {1 - {spec}} \right)}}} & {S\; 2}\end{matrix}$

Using Eq. (S1) above, one can derive sensitivity as a function of NPVand specificity, i.e.

$\begin{matrix}{{sens} = {1 - {\frac{1 - {NPV}}{NPV}\frac{1 - {prev}}{prev}{spec}}}} & \left( {S\; 3} \right)\end{matrix}$

Assume that the validation study contains N_(C) cancer samples and N_(B)benign samples. Based on binomial distribution, variances of sensitivityand specificity are given by

var(sens)=sens*(1−sens)/N _(C)  (S4)

var(spec)=spec*(1−spec)/N _(B)  (S5)

Using Eqs. (S1, S2) above, the corresponding variances of NPV and PPVcan be derived under the large-sample, normal-distribution approximationas

$\begin{matrix}{{{{var}({NPV})} = {{{NPV}^{2}\left( {1 - {NPV}} \right)}^{2}\left\lbrack {\frac{{var}({sens})}{\left( {1 - {sens}} \right)^{2}} + \frac{{var}({spec})}{{spec}^{2}}} \right\rbrack}},} & \left( {S\; 6} \right) \\{{{var}({PPV})} = {{{{PPV}^{2}\left( {1 - {PPV}} \right)}^{2}\left\lbrack {\frac{{var}({sens})}{{sens}^{2}} + \frac{{var}({spec})}{\left( {1 - {spec}} \right)^{2}}} \right\rbrack}.}} & \left( {S\; 7} \right)\end{matrix}$

The two-sided 95% CIs of sensitivity, specificity, NPV and PPV are thengiven by ±z_(α/2)√{square root over (var(sens))}, ±z_(α/2)√{square rootover (var(spec))}, ±z_(α/2)√{square root over (var(NPV))} and±z_(α/2)√{square root over (var(PPV))}, respectively, wherez_(α/2)=1.959964 is the 97.5% quantile of the normal distribution.

Experimental Procedures.

Immunoaffinity Chromatography.

An immunoaffinity column was prepared by adding 10 ml of a 50% slurrycontaining a 2:1 ratio of IgY14 and SuperMix resins (Sigma Aldrich),respectively, to a glass chromatography column (Tricorn, GE Healthcare)and allowed to settle by gravity, yielding a 5 ml volume of resin in thecolumn. The column was capped and placed on an HPLC system (Agilent 1100series) for further packing with 0.15 M (NH₄)HCO₃ at 2 ml/min for 20min, with performance assessed by replicate injections of HPS aliquots.Column performance was assessed prior to immunoaffinity separation ofeach sample batch.

To isolate low abundance proteins, 60 μl of plasma were diluted in 0.15M(NH₄)HCO₃ (1:2 v/v) to a 180 μl final volume and filtered using a 0.2 μmAcroPrep 96-well filter plate (Pall Life Sciences). Immunoaffinityseparation was conducted on a IgY14-SuperMix column connected to an HPLCsystem (Agilent 1100 series) using 3 buffers (loading/washing: 0.15 M(NH₄)HCO₃; stripping/elution: 0.1 M glycine, pH 2.5; and neutralization:0.01 M Tris-HCl and 0.15 M NaCl, pH 7.4) with a cycle comprised of load,wash, elute, neutralization and re-equilibration lasting 36 min. Theunbound and bound fractions were monitored at 280 nm and were baselineresolved after separation. Unbound fractions (containing the lowabundance proteins) were collected for downstream processing andanalysis, and lyophilized prior to enzymatic digestion.

Enzymatic Digestion and Solid-Phase Extraction.

Lyophilized fractions containing low abundance proteins were digestedwith trypsin after being reconstituted under mild denaturing conditionsin 200 μl of 1:1 0.1 M (NH₄)HCO₃/trifluoroethanol (TFE) (v/v) and thenallowed to incubate on an orbital shaker for 30 min at RT. Samples werediluted in 800 μl of 0.1 M (NH₄)HCO₃ and digested with 0.4 μg trypsin(Princeton Separations) per sample for 16 h at 37° C. and lyophilized.Lyophilized tryptic peptides were reconstituted in 350 μl of 0.01 M(NH₄)HCO₃ and incubated on an orbital shaker for 15 min at RT, followedby reduction using 30 μl of 0.05 M TCEP and incubation for 1 h at RT anddilution in 375 μl of 90% water/10% acetonitrile/0.2% trifluoroaceticacid. The extraction plate (Empore C18, 3M Bioanalytical Technologies)was conditioned according to the manufacturer's protocol, and aftersample loading were washed in 500 μl of 95% water/5% acetonitrile/0.1%trifluoroacetic acid and eluted by 200 μl of 52% water/48%acetonitrile/0.1% trifluoroacetic acid into a collection plate. Theeluate was split into 2 equal aliquots and was taken to dryness in avacuum concentrator. One aliquot was used immediately for massspectrometry, while the other was stored at −80° C. Samples werereconstituted in 12 μl of 90% water/10% acetonitrile/0.2% formic acidjust prior to LC-SRM MS analysis.

SRM-MS Analysis.

Peptide samples were separated using a capillary reversed-phase LCcolumn (Thermo BioBasic 18 KAPPA; column dimensions: 320 μm×150 mm;particle size: 5 μm; pore size: 300 Å) and a nano-HPLC system(nanoACQUITY, Waters Inc.). The mobile phases were (A) 0.2% formic acidin water and (B) 0.2% formic acid in acetonitrile. The samples wereinjected (8 μl) and separated using a linear gradient (98% A to 70% A)at 5 μl/minute for 19 min. Peptides were eluted directly into theelectrospray source of the mass spectrometer (5500 QTrap LC/MS/MS, ABSciex) operating in scheduled SRM positive-ion mode (Q1 resolution:unit; Q3 resolution: unit; detection window: 180 seconds; cycle time:1.5 seconds). Transition intensities were then integrated by softwareMultiQuant (AB Sciex). An intensity threshold of 10,000 was used tofilter out non-specific data and undetected transitions.

Normalization and Calibration of Raw SRM-MS Data.

Definition of Depletion Column Drift.

Due to changes in observed signal intensity after repetitive use of eachimmunoaffinity column, the column's performance was assessed byquantifying the transition intensity in the control HPS samples.Assuming I_(i,s) was the intensity of transition i in an HPS sample s,the drift of the sample was defined as

$\begin{matrix}{{{drift}_{s} = {{median}\left( \frac{I_{i,s} - {\hat{I}}_{s}}{{\hat{I}}_{s}} \right)}},} & \left( {S\; 8} \right)\end{matrix}$

where {hacek over (I)}_(i) was the mean value of I_(i,s) among all HPSsamples that were depleted by the same column, and the median was takenover all detected transitions in the sample. The column variability, ordrift, was defined as

drift_(col)=median(drift_(s)>0)−median(drift_(s)<0).  (S9)

Here the median was taken over all HPS samples depleted by the column.If no sample drift were greater or less than zero, the correspondingmedian was taken as 0. The median column drift was the median of driftsof all depletion columns used in the study.

Identification of Endogenous Normalizing Proteins.

The following criteria were used to identify a transition of anormalization protein: (A) possession of the highest median intensity ofall transitions from the same protein; (B) detected in all samples; (C)ranking high in reducing median technical coefficient of variation (CV),i.e. median CV of transition intensities that were measured on HPSsamples, as a normalizer; (D) ranking high in reducing median columndrift that was observed in sample depletion; and (E) possession of lowmedian technical CV and low median biological CV, i.e. median CV oftransition intensities that were measured on clinical samples. Sixendogenous normalizing proteins were identified and are listed in Table33.

TABLE 33 List of endogenous normalizing proteins Median MedianNormalizing Technical Column Protein Transition CV (%) Drift (%)PEDF_HUMAN LQSLFDSPDFSK_692.34_593.30 25.8 6.8 MASP1_HUMANTGVITSPDFPNPYPK_816.92_258.10 26.5 18.3 GELS_HUMANTASDFITK_441.73_710.40 27.1 16.8 LUM_HUMAN SLEDLQLTHNK_433.23_499.3027.1 16.1 C163A_HUMAN INPASLDK_429.24_630.30 26.6 14.6 PTPRJ_HUMANVITEPIPVSDLR_669.89_896.50 27.2 18.2 Normalization by Panel ofTransitions 25.1 9.0 Without Normalization 32.3 23.8

Normalization of Raw SRM-MS Data.

Six normalization transitions were used to normalize raw SRM-MS data toreduce sample-to-sample intensity variations within same study. Ascaling factor was calculated for each sample so that the intensities ofthe six normalization transitions of the sample were aligned with thecorresponding median intensities of all HPS samples. Assuming thatN_(i,s) is the intensity of a normalization transition i in sample s and{circumflex over (N)}_(i) the corresponding median intensity of all HPSsamples, then the scaling factor for sample s is given by Ŝ/S_(s), where

$S_{s} = {{median}\left( {\frac{N_{1,s}}{{\hat{N}}_{1}},\frac{N_{2,s}}{{\hat{N}}_{2}},\ldots \mspace{14mu},\frac{N_{6,s}}{{\hat{N}}_{6}}} \right)}$

is the median of the intensity ratios and Ŝ is the median of S_(s) overall samples in the study. Finally, for each transition of each sample,its normalized intensity was calculated as

{hacek over (I)} _(i,s) =I _(i,s) *Ŝ/S _(s)  (S11)

where I_(i,s) was the raw intensity.

Calibration by Human Plasma Standard (HPS) Samples.

For a label-free MS approach, variation on signal intensity betweendifferent experiments is expected. To reduce this variation, we utilizedHPS samples as an external standard and calibrated the intensity betweenthe discovery and validation studies. Assume that {hacek over (I)}_(i,s)is the logarithmically transformed (base 2), normalized intensity oftransition i in sample s, {hacek over (I)}_(i,dis) and {hacek over(I)}_(i,val) are the corresponding median values of HPS samples in thediscovery and the validation studies, respectively. Then the HPScorrected intensity is

Ĩ _(i,s) ={hacek over (I)} _(i,s) −{hacek over (I)} _(i,val) +{hacekover (I)} _(i,dis)  (S12)

Calculation of q-Values of Peptide and Protein Assays. In thedevelopment of SRM assays, it is important to ensure that thetransitions detected correspond to the peptides and proteins they wereintended to measure. Computational tools such as mProphet (15) enableautomated qualification of SRM assays. We introduced a complementarystrategy to mProphet that does not require customization for eachdataset. It utilizes expression correlation techniques (16) to confirmthe identity of transitions from the same peptide and protein with highconfidence. In FIG. 16, a histogram of the Pearson correlations betweenevery pair of transitions in the assay is presented. The correlationbetween a pair of transitions is obtained from their expression profilesover all samples in the discovery study. As expected, transitions fromthe same peptide are highly correlated. Similarly, transitions fromdifferent peptide fragments of the same protein are also highlycorrelated. In contrast, transitions from different proteins are nothighly correlated, which enables a statistical analysis of the qualityof a protein's SRM assay.

To determine the false positive assay rate we calculated the q-values(17) of peptide SRM assays. Using the distribution of Pearsoncorrelations between transitions from different proteins as the nulldistribution (FIG. 16), an empirical p-value was assigned to a pair oftransitions from the same peptide, detected in at least five commonsamples. A value of ‘NA’ is assigned if the pair of transitions wasdetected in less than five common samples. The empirical p-value wasconverted to a q-value using the “qvalue” package in Bioconductor(www.bioconductor.org/packages/release/bioc/html/qvalue.html). Wecalculated the q-values of protein SRM assays in the same way exceptPearson correlations of individual proteins were calculated as thosebetween two transitions from different peptides of the protein. Forproteins not having two peptides detected in five or more commonsamples, their q-values could not be properly evaluated and wereassigned ‘NA’. If the correlation of transitions from two peptides fromthe same protein is above 0.5 then there was less than a 3% probabilitythat the assay is false.

Most 36 cooperative proteins are shown in table below.

TABLE 34 Cooperative classifiers Official Protein Gene CooperativePartial Coefficient Transition for Category (UniProt) Name Score AUC CVFrequency Quantitation Classifier TSP1_HUMAN THBS1 1.8 0.25 0.24 59GFLLLASLR_495.31_559.40 Classifier COIA1_HUMAN COL18A1 3.7 0.16 0.25 91AVGLAGTFR_446.26_721.40 Classifier ISLR_HUMAN ISLR 1.4 0.32 0.25 64ALPGTPVASSQPR_640.85_841.50 Classifier TETN_HUMAN CLEC3B 2.5 0.26 0.2667 LDTLAQEVALLK_657.39_330.20 Classifier FRIL_HUMAN FTL 2.8 0.31 0.26 53LGGPEAGLGEYLFER_804.40_913.40 Classifier GRP78_HUMAN HSPA5 1.4 0.27 0.2740 TWNDPSVQQDIK_715.85_260.20 Classifier ALDOA_HUMAN ALDOA 1.3 0.26 0.2888 ALQASALK_401.25_617.40 Classifier BGH3_HUMAN TGFBI 1.8 0.21 0.28 69LTLLAPLNSVFK_658.40_804.50 Classifier LG3BP_HUMAN LGALS3BP 4.3 0.29 0.2976 VEIFYR_413.73_598.30 Classifier LRP1_HUMAN LRP1 4.0 0.13 0.32 93TVLWPNGLSLDIPAGR_855.00_400.20 Classifier FIBA_HUMAN FGA 1.1 0.31 0.3511 NSLFEYQK_514.76_714.30 Classifier PRDX1_HUMAN PRDX1 1.5 0.32 0.37 68QITVNDLPVGR_606.30_428.30 Classifier GSLG1_HUMAN GLG1 1.2 0.34 0.45 23IIIQESALDYR_660.86_338.20 Robust KIT_HUMAN KIT 1.4 0.33 0.46 28YVSELHLTR_373.21_263.10 Robust CD14_HUMAN CD14 4.0 0.33 0.48 73ATVNPSAPR_456.80_527.30 Robust EF1A1_HUMAN EEF1A1 1.2 0.32 0.56 52IGGIGTVPVGR_513.30_428.30 Robust TENX_HUMAN TNXB 1.1 0.30 0.56 22YEVTVVSVR_526.29_759.50 Robust AIFM1_HUMAN AIFM1 1.4 0.32 0.70 6ELWFSDDPNVTK_725.85_558.30 Robust GGH_HUMAN GGH 1.3 0.32 0.81 43YYIAASYVK_539.28_638.40 Robust IBP3_HUMAN IGFBP3 3.4 0.32 1.82 58FLNVLSPR_473.28_685.40 Robust ENPL_HUMAN HSP90B1 1.1 0.29 5.90 22SGYLLPDTK_497.27_460.20 Non- ERO1A_HUMAN ERO1L 6.2VLPFFERPDFQLFTGNK_685.70_318.20 Robust Non- 6PGD_HUMAN PGD 4.3LVPLLDTGDIIIDGGNSEYR_1080.60_897.40 Robust Non- ICAM1_HUMAN ICAM1 3.9VELAPLPSWQPVGK_760.93_342.20 Robust Non- PTPA_HUMAN PPP2R4 2.1FGSLLPIHPVTSG_662.87_807.40 Robust Non- NCF4_HUMAN NCF4 2.0GATGIFPLSFVK_618.85_837.50 Robust Non- SEM3G_HUMAN SEMA3G 1.9LFLGGLDALYSLR_719.41_837.40 Robust Non- 1433T_HUMAN YWHAQ 1.5TAFDEAIAELDTLNEDSYK_1073.00_748.40 Robust Non- RAP2B_HUMAN RAP2B 1.5VDLEGER_409.21_603.30 Robust Non- MMP9_HUMAN MMP9 1.4AFALWSAVTPLTFTR_840.96_290.20 Robust Non- FOLH1_HUMAN FOLH1 1.3LGSGNDFEVFFQR_758.37_825.40 Robust Non- GSTP1_HUMAN GSTP1 1.3ALPGQLKPFETLLSQNQGGK_709.39_831.40 Robust Non- EF2_HUMAN EEF2 1.3FSVSPVVR_445.76_470.30 Robust Non- RAN_HUMAN RAN 1.2LVLVGDGGTGK_508.29_591.30 Robust Non- SODM_HUMAN SOD2 1.2NVRPDYLK_335.52_260.20 Robust Non- DSG2_HUMAN DSG2 1.1GQIIGNFQAFDEDTGLPAHAR_753.04_299.20 Robust P Value (Mann- PredictedWhitney Transition for Peptide Tissue Concentration Category test)Qualification Q Value Candidate (ng/ml) Classifier 0.23GFLLLASLR_495.31_318.20 1.90E−05 510 Classifier 0.16AVGLAGTFR_446.26_551.30 6.70E−04 35 Classifier 0.74ALPGTPVASSQPR_640.85_440.30 4.40E−03 — Classifier 0.14LDTLAQEVALLK_657.39_871.50 3.70E−05 58000 Classifier 0.19LGGPEAGLGEYLFER_804.40_525.30 4.30E−05 Secreted, 12 Epi, Endo Classifier0.44 TWNDPSVQQDIK_715.85_288.10 1.80E−03 Secreted, 100 Epi, EndoClassifier 0.57 ALQASALK_401.25_489.30 3.70E−05 Secreted, 250 EpiClassifier 0.57 LTLLAPLNSVFK_658.40_875.50 1.40E−04 140 Classifier 0.45VEIFYR_413.73_485.30 2.80E−05 Secreted 440 Classifier 0.26TVLWPNGLSLDIPAGR_855.00_605.30 1.40E−04 Epi 20 Classifier 0.57NSLFEYQK_514.76_315.20 1.90E−05 130000 Classifier 0.24QITVNDLPVGR_606.30_770.40 1.90E−05 Epi 60 Classifier 0.27IIIQESALDYR_660.86_724.40 6.70E−03 Epi, — Endo Robust 0.27YVSELHLTR_373.21_526.30 2.40E−03 8.2 Robust 0.72 ATVNPSAPR_456.80_386.204.30E−04 Epi 420 Robust 0.53 IGGIGTVPVGR_513.30_628.40 4.50E−04Secreted, 61 Epi Robust 0.54 YEVTVVSVR_526.29_660.40 1.10E−03 Endo 70Robust 0.20 ELWFSDDPNVTK_725.85_875.40 3.70E−02 Epi, 1.4 Endo Robust0.24 YYIAASYVK_539.28_567.30 1.70E−03 250 Robust 0.04FLNVLSPR_473.28_359.20 2.80E−05 5700 Robust 0.57 SGYLLPDTK_497.27_573.301.10E−03 Secreted, 88 Epi, Endo Non- 0.06VLPFFERPDFQLFTGNK_685.70_419.20 1.20E−02 Secreted, — Robust Epi, EndoNon- 0.03 LVPLLDTGDIIIDGGNSEYR_1080.60_974.50 5.50E−03 Epi, 29 RobustEndo Non- 0.31 VELAPLPSWQPVGK_760.93_413.20 2.80E−02 71 Robust Non- 0.26FGSLLPIHPVTSG_662.87_292.10 1.90E−03 Endo 3.3 Robust Non- 0.11GATGIFPLSFVK_618.85_690.40 7.90E−04 Endo — Robust Non- 0.20LFLGGLDALYSLR_719.41_538.30 1.10E−03 — Robust Non- 0.69TAFDEAIAELDTLNEDSYK_1073.00_969.50 1.10E−02 Epi 180 Robust Non- 0.34VDLEGER_409.21_361.20 1.20E−03 Epi — Robust Non- 0.36AFALWSAVTPLTFTR_840.96_589.30 4.00E−03 28 Robust Non- 0.06LGSGNDFEVFFQR_758.37_597.30 5.80E−03 — Robust Non- 0.46ALPGQLKPFETLLSQNQGGK_709.39_261.20 1.70E−04 Endo 32 Robust Non- 0.79FSVSPVVR_445.76_557.30 1.10E−02 Secreted, 30 Robust Epi Non- 0.27LVLVGDGGTGK_508.29_326.20 2.80E−03 Secreted, 4.6 Robust Epi Non- 0.86NVRPDYLK_335.52_423.30 2.40E−02 Secreted 7.1 Robust Non- 0.08GQIIGNFQAFDEDTGLPAHAR_753.04_551.30 5.70E−03 Endo 2.7 Robust

A P-classifier using the same steps for the 13-protein classifierderivation (see Table 28 and Materials and Methods in Example 9) exceptthat the Mann Whitney p-value was used in place of cooperative score wasalso derived.

TABLE 35 P-Classifiers P Value Official (Mann- Coef- Cooper- ProteinGene Whitney Coefficient ficient ative Category (UniProt) NameTransition for Quantitation test) (α = 27.24) CV Protein P-ClassifierFRIL_HUMAN FTL LGGPEAGLGEYLFER_804.40_913.40 0.19 0.39 0.21 Yes P-TSP1_HUMAN THBS1 GFLLLASLR_495.31_559.40 0.23 0.48 0.21 Yes ClassifierP- LRP1_HUMAN LRP1 TVLWPNGLSLDIPAGR_855.00_400.20 0.26 −0.81 0.22 YesClassifier P- PRDX1_HUMAN PRDX1 QITVNDLPVGR_606.30_428.30 0.24 −0.510.24 Yes Classifier P- TETN_HUMAN CLEC3B LDTLAQEVALLK_657.39_330.20 0.14−1.08 0.27 Yes Classifier P- TBB3_HUMAN TUBB3 ISVYYNEASSHK_466.60_458.200.08 −0.21 0.29 No Classifier P- COIA1_HUMAN COL18A1AVGLAGTFR_446.26_721.40 0.16 −0.72 0.29 Yes Classifier P- GGH_HUMAN GGHYYIAASYVK_539.28_638.40 0.24 0.74 0.33 Yes Classifier P- A1AG1_HUMANORM1 YVGGQEHFAHLLILR_584.99_263.10 0.27 0.30 0.36 No Classifier RobustAIFM1_HUMAN AIFM1 ELWFSDDPNVTK_725.85_558.30 0.20 Yes Robust AMPN_HUMANANPEP DHSAIPVINR_374.54_402.20 0.16 No Robust CRP_HUMAN CRPESDTSYVSLK_564.77_347.20 0.17 No Robust GSLG1_HUMAN GLG1IIIQESALDYR_660.86_338.20 0.27 Yes Robust IBP3_HUMAN IGFBP3FLNVLSPR_473.28_685.40 0.04 Yes Robust KIT_HUMAN KITYVSELHLTR_373.21_263.10 0.27 Yes Robust NRP1_HUMAN NRP1SFEGNNNYDTPELR_828.37_514.30 0.22 No Non- 6PGD_HUMAN PGDLVPLLDTGDIIIDGGNSEYR_1080.60_897.4 0.03 Yes Robust Non- CH10_HUMAN HSPE1VLLPEYGGTK_538.80_751.40 0.07 No Robust Non- CLIC1_HUMAN CLIC1FSAYIK_364.70_581.30 0.14 No Robust Non- COF1_HUMAN CFL1YALYDATYETK_669.32_827.40 0.08 No Robust Non- CSF1_HUMAN CSF1ISSLRPQGLSNPSTLSAQPQLSR_813.11_600.30 0.23 No Robust Non- CYTB_HUMANCSTB SQVVAGTNYFIK_663.86_315.20 0.16 No Robust Non- DMKN_HUMAN DMKNVSEALGQGTR_509.27_631.40 0.17 No Robust Non- DSG2_HUMAN DSG2GQIIGNFQAFDEDTGLPAHAR_753.04_299.20 0.08 Yes Robust Non- EREG_HUMAN EREGVAQVSITK_423.26_448.30 0.16 No Robust Non- ERO1A_HUMAN ERO1LVLPFFERPDFQLFTGNK_685.70_318.20 0.06 Yes Robust Non- FOLH1_HUMAN FOLH1LGSGNDFEVFFQR_758.37_825.40 0.06 Yes Robust Non- ILEU_HUMAN SERPINB1TYNFLPEFLVSTQK_843.94_379.20 0.09 No Robust Non- K1C19_HUMAN KRT19FGAQLAHIQALISGIEAQLGDVR_803.11_274.20 0.17 No Robust Non- LYOX_HUMAN LOXTPILLIR_413.28_514.40 0.22 No Robust Non- MMP7_HUMAN MMP7LSQDDIK_409.72_705.30 0.23 No Robust Non- NCF4_HUMAN NCF4GATGIFPLSFVK_618.85_837.50 0.11 Yes Robust Non- PDIA3_HUMAN PDIA3ELSDFISYLQR_685.85_779.40 0.04 No Robust Non- PTGIS_HUMAN PTGISLLLFPFLSPQR_665.90_340.30 0.06 No Robust Non- PTPA_HUMAN PPP2R4FGSLLPIHPVTSG_662.87_807.40 0.26 Yes Robust Non- RAN_HUMAN RANLVLVGDGGTGK_508.29_591.30 0.27 Yes Robust Non- SCF_HUMAN KITLGLFTPEEFFR_593.30_261.20 0.16 No Robust Non- SEM3G_HUMAN SEMA3GLFLGGLDALYSLR_719.41_837.40 0.20 Yes Robust Non- TBA1B_HUMAN TUBA1BAVFVDLEPTVIDEVR_851.50_928.50 0.15 No Robust Non- TCPA_HUMAN TCP1IHPTSVISGYR_615.34_251.20 0.17 No Robust Non- TERA_HUMAN VCPGILLYGPPGTGK_586.80_284.20 0.29 No Robust Non- TIMP1_HUMAN TIMP1GFQALGDAADIR_617.32_717.40 0.26 No Robust Non- TNF12_HUMAN TNFSF12AAPFLTYFGLFQVH_805.92_700.40 0.29 No Robust Non- UGPA_HUMAN UGP2LVEIAQVPK_498.80_784.50 0.08 No Robust

REFERENCES

-   1. Albert & Russell Am Fam Physician 80:827-831 (2009)-   2. Gould et al. Chest 132:108 S-130S (2007)-   3. Kitteringham et al. J Chromatrog B Analyt Technol Biomed Life Sci    877:1229-1239 (2009)-   4. Lange et al. Mol Syst Biol 4:222 (2008)-   5. Lehtio & De Petris J Proteomics 73:1851-1863 (2010)-   6. MacMahon et al. Radiology 237:395-400 (2005)-   7. Makawita Clin Chem 56:212-222 (2010)-   8. Ocak et al. Proc Am Thorac Soc 6:159-170 (2009)-   9. Ost, D. E. and M. K. Gould, Decision making in patients with    pulmonary nodules. Am J Respir Crit. Care Med, 2012. 185(4): p.    363-72.-   10. Cima, I., et al., Cancer genetics-guided discovery of serum    biomarker signatures for diagnosis and prognosis of prostate cancer.    Proc Natl Acad Sci USA, 2011. 108(8): p. 3342-7.-   11. Desiere, F., et al., The PeptideAtlas project. Nucleic Acids    Res, 2006. 34 (Database issue): p. D655-8.-   12. Farrah, T., et al., A high-confidence human plasma proteome    reference set with estimated concentrations in PeptideAtlas. Mol    Cell Proteomics, 2011. 10(9): p. M110 006353.-   13. Omenn, G. S., et al., Overview of the HUPO Plasma Proteome    Project: results from the pilot phase with 35 collaborating    laboratories and multiple analytical groups, generating a core    dataset of 3020 proteins and a publicly-available database.    Proteomics, 2005. 5(13): p. 3226-45.-   14. Kearney, P., et al., Protein identification and Peptide    expression resolver: harmonizing protein identification with protein    expression data. J Proteome Res, 2008. 7(1): p. 234-44.-   15. Huttenhain, R., et al., Reproducible quantification of    cancer-associated proteins in body fluids using targeted proteomics.    Sci Transl Med, 2012. 4(142): p. 142ra94.-   16. Henschke, C. I., et al., CT screening for lung cancer:    suspiciousness of nodules according to size on baseline scans.    Radiology, 2004. 231(1): p. 164-8.-   17. Henschke, C. I., et al., Early Lung Cancer Action Project:    overall design and findings from baseline screening. Lancet, 1999.    354(9173): p. 99-105.-   18. States, D. J., et al., Challenges in deriving high-confidence    protein identifications from data gathered by a HUPO plasma proteome    collaborative study. Nat Biotechnol, 2006. 24(3): p. 333-8.-   19. Polanski, M. and N. L. Anderson, A list of candidate cancer    biomarkers for targeted proteomics. Biomark Insights, 2007. 1: p.    1-48.-   20. Krogh, A., et al., Predicting transmembrane protein topology    with a hidden Markov model: application to complete genomes. J Mol    Biol, 2001. 305(3): p. 567-80.-   21. Bendtsen, J. D., et al., Improved prediction of signal peptides:    SignalP 3.0. J Mol Biol, 2004. 340(4): p. 783-95.-   22. Bendtsen, J. D., et al., Feature-based prediction of    non-classical and leaderless protein secretion. Protein Eng Des    Sel, 2004. 17(4): p. 349-56.-   23. Lange, V., et al., Selected reaction monitoring for quantitative    proteomics: a tutorial. Mol Syst Biol, 2008. 4: p. 222.-   24. Picotti, P., et al., High-throughput generation of selected    reaction-monitoring assays for proteins and proteomes. Nat    Methods, 2010. 7(1): p. 43-6.-   25. Mallick, P., et al., Computational prediction of proteotypic    peptides for quantitative proteomics. Nat Biotechnol, 2007.    25(1): p. 125-31.-   26. Perkins, D. N., et al., Probability-based protein identification    by searching sequence databases using mass spectrometry data.    Electrophoresis, 1999. 20(18): p. 3551-67.-   27. Hastie, T., R. Tibshirani, and J. H. Friedman, The elements of    statistical learning: data mining, inference, and prediction: with    200 full-color illustrations. Springer series in statistics. 2001,    New York: Springer. xvi, 533 p.-   28. McClish, D. K., Analyzing a portion of the ROC curve. Med Decis    Making, 1989. 9(3): p. 190-5.

What is claimed is:
 1. A method of determining the likelihood that alung condition in a subject is cancer, comprising: (a) measuring anabundance of a panel of proteins in a sample obtained from the subject,wherein said panel comprises at least 3 proteins selected from the groupconsisting of ALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78,TETN, PRDX1 and CD14; (b) calculating a probability of cancer scorebased on the protein measurements of step (a); and (c) ruling out cancerfor the subject if the score in step (b) is lower than a pre-determinedscore.
 2. The method of claim 1, wherein said panel further comprises atleast one protein selected from the group consisting of BGH3, FIBA andGSLG1.
 3. The method of claim 1, wherein said panel comprises at least 4proteins.
 4. The method of claim 3, wherein said panel comprises LRP1,COIA1, ALDOA, and LG3BP.
 5. The method of claim 1, wherein said panelcomprises LRP1, COIA1, ALDOA, LG3BP, BGH3, PRDX1, TETN, and ISLR.
 6. Themethod of claim 1, wherein said panel comprises LRP1, COIA1, ALDOA,LG3BP, BGH3, PRDX1, TETN, ISLR, TSP1, GRP78, FRIL, FIBA and GSLG1. 7.The method of claim 1, wherein when cancer is ruled out the subject doesnot receive a treatment protocol.
 8. The method of claim 7, wherein saidtreatment protocol is a pulmonary function test (PFT), pulmonaryimaging, a biopsy, a surgery, a chemotherapy, a radiotherapy, or anycombination thereof.
 9. The method of claim 8, where said imaging is anx-ray, a chest computed tomography (CT) scan, or a positron emissiontomography (PET) scan.
 10. The method of claim 1, wherein said subjecthas a pulmonary nodule.
 11. The method of claim 10, wherein saidpulmonary nodule has a diameter of less than or equal to 3 cm.
 12. Themethod of claim 10, wherein said pulmonary nodule has a diameter ofabout 0.8 cm to 2.0 cm.
 13. The method of claim 1, wherein said score iscalculated from a logistic regression model applied to the proteinmeasurements.
 14. The method of claim 1, wherein said score isdetermined as P_(s)=1/[1+exp(−α−Σ_(i=1) ^(N)β_(i)*{hacek over(I)}_(i,s))], where {hacek over (I)}_(i,s) is logarithmicallytransformed and normalized intensity of transition i in said sample (s),β_(i) is the corresponding logistic regression coefficient, α was apanel-specific constant, and N was the total number of transitions insaid panel.
 15. The method of claim 13, further comprising normalizingthe protein measurements.
 16. The method of claim 15, wherein theprotein measurements are normalized by one or more proteins selectedfrom the group consisting or PEDF, MASP1, GELS, LUM, C163A and PTPRJ.17. The method of claim 1, wherein said biological sample is selectedfrom the group consisting of tissue, blood, plasma, serum, whole blood,urine, saliva, genital secretion, cerebrospinal fluid, sweat andexcreta.
 18. The method of claim 1, wherein the determining thelikelihood of cancer is determined by the sensitivity, specificity,negative predictive value or positive predictive value associated withthe score.
 19. The method of claim 1, wherein said score determined instep (a) has a negative predictive value (NPV) is at least about 80%.20. A method of ruling in the likelihood of cancer for a subject,comprising: (a) measuring an abundance of panel of proteins in a sampleobtained from the subject, wherein said panel comprising at least 3proteins selected from the group consisting of ALDOA, FRIL, LG3BP, IBP3,LRP1, ISLR, TSP, COIA1, GRP78, TETN, PRDX1 and CD14; and (b) calculatinga probability of cancer score based on the protein measurements of step(a); and (c) ruling in the likelihood of cancer for the subject if thescore in step (b) is higher than a pre-determined score.
 21. The methodof claim 20, wherein said panel further comprises at least one proteinselected from the group consisting of BGH3, FIBA and GSLG1.
 22. Themethod of claim 20, wherein said panel comprises at least 4 proteins.23. The method of claim 22, wherein said panel comprises LRP1, COIA1,ALDOA, and LG3BP.
 24. The method of claim 20, wherein said panelcomprises LRP1, COIA1, ALDOA, LG3BP, BGH3, PRDX1, TETN, and ISLR. 25.The method of claim 20, wherein said panel comprises LRP1, COIA1, ALDOA,LG3BP, BGH3, PRDX1, TETN, ISLR, TSP1, GRP78, FRIL, FIBA and GSLG1. 26.The method of claim 20, wherein when cancer is ruled in the subjectreceives a treatment protocol.
 27. A method of determining thelikelihood of the presence of a lung condition in a subject, comprising:(a) measuring an abundance of panel of proteins in a sample obtainedfrom the subject, wherein said panel comprising at least 3 proteinsselected from the group consisting of ALDOA, FRIL, LG3BP, IBP3, LRP1,ISLR, TSP, COIA1, GRP78, TETN, PRDX1 and CD14; (b) calculating aprobability of cancer score based on the protein measurements of step(a); and (c) concluding the presence of said lung condition if the scoredetermined in step (b) is equal or greater than a pre-determined score.28. The method of claim 27, wherein said lung condition is lung cancer.29. The method of claim 28, wherein said lung cancer is non-small celllung cancer (NSCLC).
 30. The method of claim 1, wherein the measuringstep is performed by selected reaction monitoring mass spectrometry,using a compound that specifically binds the protein being detected or apeptide transition.
 31. The method of claim 30, wherein the compoundthat specifically binds to the protein being measures is an antibody oran aptamer.
 32. The method of claim 27, wherein the subject is at riskof developing lung cancer.
 33. The method of claim 27, wherein saidpanel comprises at least 4 proteins.
 34. The method of claim 33, whereinsaid panel comprises LRP1, COIA1, ALDOA, and LG3BP.
 35. The method ofclaim 27, wherein said panel comprises LRP1, COIA1, ALDOA, LG3BP, BGH3,PRDX1, TETN, and ISLR.
 36. The method of claim 27, wherein said panelcomprises LRP1, COIA1, ALDOA, LG3BP, BGH3, PRDX1, TETN, ISLR, TSP1,GRP78, FRIL, FIBA and GSLG1.